The Dependence of Daily and Hourly Precipitation Extremes on Temperature and Atmospheric Humidity over China

Hong Wang Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

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Fubao Sun Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, and Research School of Qilian Mountain Ecology, Hexi University, Zhangye City, and College of Resources and Environment, University of Chinese Academy of Sciences, and Center for Water Resources Research, Chinese Academy of Sciences, Beijing, China

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Wenbin Liu Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

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Abstract

Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation. However, this scaling behavior is inappropriate for high temperatures and short-duration precipitation extremes. Here, daily data from 702 stations during 1951–2014 and hourly data from 8 stations during 2000–15 are used to examine and explain this behavior in China. Both daily and hourly precipitation extremes exhibit an increase in temperature dependency at lower temperatures. The CC scaling transitions from positive to negative rates with temperatures greater than 25°C. Unlike the increase in daily data, which is similar to single-CC (1CC) scaling, the increase in hourly data resembles super-CC (2CC) scaling for temperatures greater than 13°C. Results show that the precipitation extremes are controlled by water vapor for a given temperature. At lower temperatures, precipitation extremes exhibit a positive linear dependence on daily actual vapor pressure whose value is almost equal to the saturated vapor pressure at a given temperature. At higher temperatures, actual vapor pressure has difficulty maintaining a consistent increasing rate because of the exponential increasing of the saturated vapor pressure. Higher temperatures result in larger vapor pressure deficits, which lead to sharp decreases in precipitation extremes. Similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature increases from 17°C along the northwest inland area to 25°C along the southeast coast. These behaviors demonstrate that precipitation extremes are firmly linked to temperature when there is sufficient moisture at lower temperatures and limited by insufficient moisture at higher temperatures. Overall, precipitation extreme events require more attention in a warming climate.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0050.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Fubao Sun, sunfb@igsnrr.ac.cn

Abstract

Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation. However, this scaling behavior is inappropriate for high temperatures and short-duration precipitation extremes. Here, daily data from 702 stations during 1951–2014 and hourly data from 8 stations during 2000–15 are used to examine and explain this behavior in China. Both daily and hourly precipitation extremes exhibit an increase in temperature dependency at lower temperatures. The CC scaling transitions from positive to negative rates with temperatures greater than 25°C. Unlike the increase in daily data, which is similar to single-CC (1CC) scaling, the increase in hourly data resembles super-CC (2CC) scaling for temperatures greater than 13°C. Results show that the precipitation extremes are controlled by water vapor for a given temperature. At lower temperatures, precipitation extremes exhibit a positive linear dependence on daily actual vapor pressure whose value is almost equal to the saturated vapor pressure at a given temperature. At higher temperatures, actual vapor pressure has difficulty maintaining a consistent increasing rate because of the exponential increasing of the saturated vapor pressure. Higher temperatures result in larger vapor pressure deficits, which lead to sharp decreases in precipitation extremes. Similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature increases from 17°C along the northwest inland area to 25°C along the southeast coast. These behaviors demonstrate that precipitation extremes are firmly linked to temperature when there is sufficient moisture at lower temperatures and limited by insufficient moisture at higher temperatures. Overall, precipitation extreme events require more attention in a warming climate.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0050.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Fubao Sun, sunfb@igsnrr.ac.cn

1. Introduction

Evidence that precipitation extremes are increasing globally has strengthened considerably in recent years (Berg et al. 2013; Fischer and Knutti 2016; Lenderink and Meijgaard 2008; Westra et al. 2014). Precipitation extremes are expected to increase by 7% per degree of warming according to the Clausius–Clapeyron (CC) relation, which states that the atmosphere can hold approximately 6%–7% more water vapor per degree of warming before saturation occurs (Lenderink and Meijgaard 2008; Trenberth et al. 2003). However, this scaling behavior does not appear to be appropriate for high temperatures (Busuioc et al. 2017; Chan et al. 2016; Drobinski et al. 2016; Wang et al. 2017) and short-duration precipitation extremes (Haerter and Berg 2009; Jones et al. 2010; Lenderink and Meijgaard 2008).

Negative scaling behaviors because of high temperatures for both daily and subdaily precipitation extremes have been found in the Amazon and Congo basins, the tropical Pacific, the Indian monsoon region, the U.S. Midwest, central Europe (Wang et al. 2017), Romania (Busuioc et al. 2017), the United Kingdom (Chan et al. 2016), the Mediterranean (Drobinski et al. 2016), Australia (Jones et al. 2010), Japan (Utsumi et al. 2011), South Korea (Park and Min 2017), and China (Huang et al. 2017; Sun et al. 2013; Xiao et al. 2016). Additionally, the breaking point temperature (the temperature for the CC scaling transition from positive to negative rates) has been found to be different in different study areas, and it is ~22°C in the United Kingdom (Chan et al. 2016), 20°C in the Mediterranean (Drobinski et al. 2016), 26°C in Australia (Jones et al. 2010), ~20°–24°C in southeastern Austria (Schroeer and Kirchengast 2018), 25°C in China (Huang et al. 2017; Sun et al. 2013; Xiao et al. 2016), and 18°C during spring and summer at the 99.9th percentile in Romania (Busuioc et al. 2017). Short-duration precipitation extremes have exceeded CC relation expectations, and a transitional behavior from single-CC (1CC) to super-CC (2CC) scaling for hourly precipitation extremes has been found (Haerter and Berg 2009; Lenderink and Meijgaard 2008). Station observations suggested that 1-h precipitation extremes increase twice as fast with rising temperatures, as expected from the 1CC relation, when daily mean temperatures exceed 12°C (Haerter and Berg 2009; Lenderink and Meijgaard 2008).

Model predictions have shown that future daily precipitation extremes will continue to intensify (Donat et al. 2016), and the observed scaling rates of precipitation extremes are found to be larger than that predicted by models, implying that future changes in precipitation extremes may still be underestimated (Allan and Soden 2008) because of the change in water vapor (Bao et al. 2017). Barbero et al. (2017) have suggested that it may be helpful to determine whether or not future changes in precipitation extremes are likely to exceed observed scaling rates that are estimated using dewpoint temperature variations rather than surface temperature. Future hourly precipitation extremes cannot simply be extrapolated from daily precipitation extremes scaling analyses, and the models cannot provide a reliable extrapolation basis until improved methods are available (Chan et al. 2016; Zhang et al. 2017).

Atmospheric humidity, precipitation type and duration, microphysics, El Niño events, orography, anthropogenic aerosols, and greenhouse gases have been thought to influence the scaling behaviors of precipitation extremes at higher temperatures or for short durations. First, atmospheric moisture availability with increasing temperatures has been identified as a particularly large influence on scaling. The increase in daily precipitation intensity has also been strongly associated with the increase in water vapor (Ye et al. 2015). Both observations and models show a strong reduction in relative humidity at higher temperatures, which reflects how moisture availability becomes the dominant driver in extreme precipitation events (Jones et al. 2010; Sun et al. 2015; Xiao et al. 2016). Several results have shown that the dewpoint temperature has been used as an alternative measure with which to investigate the combined effect of atmospheric temperature and moisture availability that drives precipitation extremes (Barbero et al. 2017; Westra et al. 2014). Trenberth et al. (2003) have argued that because heavy rainfall rates greatly exceed evaporation rates and thus depend on low-level moisture convergence, the rainfall intensity should also increase at about the same rate as the moisture increases, namely, 7% K−1 with warming. In fact, the rate of increase can even exceed this because the additional latent heat released feeds back and invigorates the storm that causes the rain in the first place, further enhancing convergence of moisture (Trenberth et al. 2003; Liu et al. 2009). Therefore, precipitation extremes increase with temperature in moist, energy-limited environments and decrease abruptly in dry, moisture-limited environments (Prein et al. 2017). Second, precipitation can be separated into convective and stratiform events based on cloud observations (Berg et al. 2013) or the presence of lightning (Molnar et al. 2015). Convective precipitation has a more sensitive response to temperature increases than that from stratiform precipitation, which has increasingly dominated subdaily precipitation extremes (Berg et al. 2013; Busuioc et al. 2017; Molnar et al. 2015; Singleton and Toumi 2013) because of the increased propagation of squall lines (Singleton and Toumi 2013); these events exhibit typical convective precipitation structures, which scaling breaks down at higher temperatures (Singleton and Toumi 2013). And results of models and simulations also imply that the convective systems drive precipitation extremes (Meredith et al. 2015; Moseley et al. 2016). Other results have shown that CC scaling is also mainly dependent on precipitation duration (Utsumi et al. 2011; Sun et al. 2013), cloud and precipitation microphysics (Singh and O’Gorman 2014), El Niño events (Allen and Luptowitz 2017), orography (Drobinski et al. 2016), and anthropogenic aerosols and greenhouse gases (Chen et al. 2011; Min et al. 2011).

Intense precipitation extreme events create complex processes and involve multiple factors, which makes it challenging to examine and explain their behaviors, especially for China. Research in this area is quite poor and China includes many climatic regions, which makes the problem even more complicated. Here, we used temperature, precipitation, and water-vapor-related variables [actual vapor pressure ea, specific humidity q, relative humidity (Rh), and pan evaporation Epan] to study the dependence of daily and hourly precipitation extremes on temperature and atmospheric humidity over China. In section 2, we describe the data and methods. Section 3 reports the results of the relationship between temperature and precipitation extremes and attempts to explain the results quantitatively, which is then followed by the conclusion. The abbreviation of variables in the manuscript is shown in the appendix.

2. Data and methods

Figure 1 shows the spatial locations of the meteorological stations. Figure 1a shows the daily data stations and 10 river basins in China. Figure 1b shows the hourly data stations. The daily surface data during 1951–2014 are obtained from 702 meteorological stations associated with the China Meteorological Data Service Center (CMDC; http://data.cma.cn; Fig. 1a). The data have been strictly quality controlled (CMDC 2012) and contain approximately 6 325 552 data points. Daily precipitation amounts larger than 0.1 mm day−1 are defined as precipitation events. The information of the daily database and its precipitation events is shown in Table 1. The Epan is observed by a D20 pan (i.e., an evaporative pan), which has a 20-cm diameter and 10-cm-high walls.

Fig. 1.
Fig. 1.

The locations of meteorological stations. (a) Daily meteorological stations and 10 river basins over China. (b) Hourly meteorological stations, including ASA, CWA, LCA, YGA, NMD, SPD, GGF, and QIA.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

Table 1.

The information of the daily database and its precipitation events.

Table 1.

Hourly surface data from 2000 to 2015 are obtained by eight field experiments and monitoring stations (Fig. 1b), including Ansai (ASA), Changwu (CWA), Luancheng (LCA), Yanting (YGA), Naiman (NMD), Shapotou (SPD), Gongga Shan (GGF), and Qiyang (QIA), China, which are provided by the Chinese Ecosystem Research Network (CERN; http://www.cnern.org.cn) and support ecological-, climatic-, and agricultural-related research (Wang et al. 2016; Lu et al. 2016; Zhang et al. 2016). The data are measured hourly with the Milos 520 data collection and processing system, which is an automatic weather station produced by the Vaisala Company (Finland) and has field-proven reliability and accuracy (http://www.vaisala.com/en/Pages/default.aspx). The data have been strictly quality controlled and contain approximately 592 841 data points, of which 6.38% have intensities greater than 0.1 mm h−1.

Here, we reproduce the CC scaling method using the Lenderink and Meijgaard (2008) methodology. Daily precipitation intensity Id is binned using daily mean temperature at 2-m height T, and hourly precipitation intensity Ih and q are binned using Td or the hourly mean temperature at 2-m Th, where the width of each bin is 2°C. Wet events are defined by hours with precipitation values > 0.1 mm h−1. The 90th confidence intervals are estimated by the bootstrap method. The 99.9th, 99th, 95th, 90th, and 75th percentiles computed from the raw data falling within the confidence interval are estimated from the bootstrap method, where the bootstrap results are based on 1000 bootstrap samples using the International Business Machines Corporation (IBM) Statistical Product and Service Solutions (SPSS) Statistics, version 22.0. The hourly q is calculated from the hourly ea and atmospheric pressure at each station (Ye et al. 2015).

3. Results and discussion

a. The dependence of daily precipitation extremes on temperature and atmospheric humidity

1) The relationship between daily precipitation extremes and temperature

Figure 2 shows the 99.9th, 99th, 95th, 90th, and 75th percentiles of daily precipitation intensity Id compared with the CC relation. Different percentiles of Id exhibit similar scaling behaviors. For T roughly below 25°C, the different percentiles of Id exhibit a temperature dependency similar to that of the 1CC relation. While T is larger than 25°C, Id sharply decreases with an increase in temperature. This positive–negative scaling behavior is consistent with results found in the Amazon and Congo basins, the tropical Pacific, the Indian monsoon region, the U.S. Midwest, central Europe (Wang et al. 2017), Romania (Busuioc et al. 2017), the United Kingdom (Chan et al. 2016), the Mediterranean (Drobinski et al. 2016), Australia (Jones et al. 2010), Japan (Utsumi et al. 2011), and South China (Sun et al. 2013).

Fig. 2.
Fig. 2.

Daily precipitation intensity Id dependence on T. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

China includes many climatic regions, and the 10 river basins in this study divide and represent these different climate zones. To examine if this scaling behavior is the same in different regions, we analyze the relationships between different percentiles of Id and T, which results in similar scaling behaviors among different regions (Fig. 3). When T is lower than the breaking point temperature, Id exhibits a positive 1CC relation dependence on temperature. Figures 3h–j exhibit a weaker 1CC scaling behavior. Figures 3h–j contain rivers in the southeast, southwest, and northwest, so each of them has multiple basins, while the other panels contain only one basin (e.g., the rivers of Fig. 3a belong to the Chang River basin). It may be because multiple basins cause unstable relationships between Id and T. Figure 3j, which encompasses the northwest region that crosses the Qinghai–Tibetan Plateau, shows a transition from a 1CC scaling to a lower-rated scaling for higher temperature. Schroeer and Kirchengast (2018) also found that temperature sensitivities in the mountainous western region of China were weaker than those in the eastern lowlands of southeastern Austria; this was most likely because moisture was not locally sourced, which led to weaker temperature sensitivities (Schroeer and Kirchengast 2018; Zhang et al. 2017). Each basin has a distinct breaking point, where the breaking point temperature ranges from 17° to 25°C (Fig. 3k), which is consistent with values found in other study areas [e.g., 22°C in the United Kingdom (Chan et al. 2016), 20°C in the Mediterranean (Drobinski et al. 2016), 26°C in Australia (Jones et al. 2010), 25°C in South China (Sun et al. 2013), and 18°C in Romania (Busuioc et al. 2017)]. More importantly, the breaking point temperature gradually increases from the northwest inland area to the southeast coast, which is consistent with increasing patterns of precipitation, cloud amount, total cloud cover, and Rh across China. This pattern suggests that the breaking point temperature depends on water vapor availability for the occurrence of precipitation and on the distance from the basin to the coast.

Fig. 3.
Fig. 3.

(a)–(j) Daily precipitation intensity dependence on temperature over 10 river basins in China. (k) The breaking point temperatures of the 10 river basins. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

2) The relationship between atmospheric humidity and temperature

Actual vapor pressure is the partial pressure of water vapor at a given temperature. Relative humidity measures how much water the air currently contains as compared to what it would contain if saturated. Evaporation reflects part of the vertical transport of water vapor. All of these are different ways of measuring the amount of water vapor in the atmosphere. We use the abovementioned method to analyze the relationships among ea, Rh, Epan, and T under the 99.9th, 99th, 95th, 90th, and 75th percentiles. Figure 4a shows different ea percentiles compared with the CC relation. For lower temperatures, ea is strictly controlled by the CC curve and increases at an approximate rate of 7% per degree of warming. When T increases to approximately 25°C, a breaking point occurs. When T continually increases, the temperature dependency of ea transitions from a 1CC relation to a 0.5CC (or smaller) relation.

Fig. 4.
Fig. 4.

Temperature relationships with daily precipitation, humidity, and evaporation: (a) ea, (b) Rh, and (c) Epan. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line represents the breaking point temperature.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

Figure 4b shows changes of Rh for different percentiles. Relative humidity begins to increase as T increases, then gradually stabilizes at high levels, and sharply decreases when T is greater than 25°C. Both observations and previous model results have shown similar reductions in Rh at high temperatures (Jones et al. 2010; Sun et al. 2015; Xiao et al. 2016). This scaling behavior is consistent with that of ea because saturated vapor pressure es increases exponentially with temperature.

Figure 4c shows changes of Epan for different percentiles. Pan evaporation linearly increases with temperature at lower temperatures, and when T is greater than 25°C, the rate of Epan rapidly increases. The different percentiles of Epan show the same trend with T increasing. They are positively correlated with vapor pressure deficits (i.e., the difference between es and ea; Fig. 5), which are consistent with previous research results and related Epan calculations or models, such as Dalton’s (Dalton 1802) and Penman’s (Allan et al. 1998) equations of evaporation from water surface.

Fig. 5.
Fig. 5.

Percentiles for Epan vs vapor pressure deficit. The vapor pressure deficit is the difference between es and ea. Scatterplots represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Solid lines and equations explain the trend lines and fitted formulas of the scatterplots.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

The relationships among daily Id, Rh, ea, Epan, and T under the 99.9th, 99th, 95th, 90th, and 75th percentiles are analyzed in the 10 basins. Figure 6 shows the result of the 99.9th percentile, which indicates that the corresponding breaking point temperatures for Id, Rh, ea, and Epan are identical in the same basin. When T is lower than the breaking point temperature and as T increases precipitation extremes increase, ea increases strictly along the CC curve, Rh is almost stable at 100%, and the increasing rate of Epan stabilizes. When T is greater than the breaking point temperature, Id and Rh decrease rapidly, the increasing rate of ea slows down, and the Epan rate begins to accelerate. The 99th, 95th, 90th, and 75th percentiles for the 10 basins are shown in Figs. S1–S4 in the online supplemental material.

Fig. 6.
Fig. 6.

(a)–(j) The 99.9th percentile for Id, ea, Rh, and Epan in 10 river basins. (k) The breaking point temperatures for the 10 river basins. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line represents the breaking point temperature.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

3) The relationship between daily precipitation extremes and water vapor

To show the direct relationship between Id extremes and water vapor, Id is binned using daily ea. The width of each bin is 2 hPa. The ea effect at the 99.9th, 99th, 95th, 90th, and 75th percentiles on Id is shown in Fig. 7a (solid color lines). The different percentiles of Id exhibit a positive–negative ea dependency similar to that of the Id ~ T relation (Fig. 2). When ea is lower than the breaking point, Id exhibits a positive linear dependence on ea, and the correlation coefficient is not less than 0.98 (Fig. 7b).

Fig. 7.
Fig. 7.

Daily precipitation intensity Id dependence on ea. (a) Different percentiles of Id vs ea (solid color lines) and es (dash-dot-dot color lines). The medium gray dash line is the es value at a given T. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. (b) Different percentiles of Id vs ea when ea is lower than the breaking point. The color equations explain the trend lines and fitted formulas of the corresponding color scatterplots.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

Because es is the maximum partial pressure of water vapor in the air and varies with the temperature of the air and water vapor mixture, T and ea are connected by es for studying precipitation extremes. Daily precipitation intensity is binned using daily es, and the width of each bin is 2 hPa. The relationship of Id and daily es at different percentiles is shown in Fig. 7a (dash-dot-dot color lines). The two lines are very similar and have the same trend, and ea is slightly smaller than es for a given Id under the same percentiles.

The medium gray dashed line in Fig. 7a is the es value at a given temperature based on the Goff–Gratch equation (Goff and Gratch 1946; Goff 1957), which described how to calculate the es above a flat, free-water surface as a function of temperature and was recommended for use by the World Meteorological Organization in 1988, with further corrections in 2000. Figure 7a shows that the breaking points for ea and es are 29.03 and 30.93 hPa, and its corresponding breaking point temperature is 24.6°C, which is close to 25°C of the Id ~ T relation (Fig. 2).

Overall, because precipitation extremes tend to occur when the atmosphere is close to saturation at lower temperatures, precipitation extremes increase with warming in proportion to the increase in surface moisture storage capacity, which exponentially increases at a 1CC scaling rate as temperature increases (Singh and O’Gorman 2014; Wang et al. 2017). In this situation, precipitation extremes respond to temperature changes in the absence of moisture limitation. When the temperature is higher than the breaking point temperature (i.e., higher temperature), ea has difficulty maintaining a consistent increasing rate as es increases exponentially, so higher temperatures result in larger vapor pressure deficits, which leads to a sharp increase in precipitation extremes. And similar scaling behaviors are obtained in 10 river basins over China, where the breaking point temperature depends on the distance from the coast to the basin and increases from 17°C along the northwest inland area to 25°C along the southeast coast. This pattern suggests that insufficient moisture availability inhibits the development of precipitation extremes.

b. The dependence of hourly precipitation extremes on temperature and atmospheric humidity

1) The relationship between hourly precipitation extremes and temperature

Figure 8 shows the 99.9th, 99th, 95th, 90th, and 75th percentiles for hourly precipitation intensity Ih under T, daily maximum air temperature Tdmax, minimum air temperature Tdmin temperature at 2-m height in a day, and Th. The scaling behaviors among Ih and T (Fig. 8a), Tdmax (Fig. 8c), Tdmin (Fig. 8d), and Th (Fig. 8b) are generally similar. Hourly precipitation intensity increases at lower temperatures, then fluctuates from positive to negative under temperatures exceeding the breaking point temperature, indicating scaling similar to that for daily temperatures. The corresponding breaking point temperatures are approximately 23°–25°C for T, Tdmin, and Th, but temperatures are higher for Tdmax. However, the increasing rates of Id and Ih are different when the temperature is lower than the breaking point temperature. Daily precipitation intensity exhibits a temperature dependency similar to the 1CC rate. Hourly precipitation intensity exhibits an increasing rate similar to the 1CC relation at lower temperatures, which increases to a 2CC relation at higher temperatures. This scaling behavior is more distinct for higher percentiles (i.e., 99.9th and 99th) and weaker for lower percentiles (75th and 90th). The corresponding T transitions from 1CC to 2CC are approximately 13°C for the 99.9th and 99th percentiles and 17°C for the 95th percentile. The corresponding Th transitions are approximately 11°C for the 99.9th and 99th percentiles and 13°C for the 95th percentile. This transition behavior for Ih from a 1CC to 2CC scaling (when T exceeds approximately 12°C) has been identified (Haerter and Berg 2009; Lenderink and Meijgaard 2008). And some results imply that the convective field or systems drive this scaling based on cloud observations (Berg et al. 2013), the presence of lightning (Molnar et al. 2015), or simulations (Meredith et al. 2015; Moseley et al. 2016). Because the atmospheric temperature at 2 m is different from the temperature at the height of clouds, this difference in temperature may also affect extreme precipitation analysis. Atmospheric humidity (Trenberth et al. 2003; Liu et al. 2009), precipitation type (Berg et al. 2013) and duration (Utsumi et al. 2011; Sun et al. 2013), cloud and precipitation microphysics (Singh and O’Gorman 2014), El Niño events (Allen and Luptowitz 2017), orography (Drobinski et al. 2016), and anthropogenic aerosols and greenhouse gases (Chen et al. 2011; Min et al. 2011) have been thought to influence the scaling behaviors of precipitation extremes at higher temperatures or for short durations.

Fig. 8.
Fig. 8.

Hourly precipitation intensity dependence on temperature. (a) Observed 1-h precipitation intensity vs T, (b) Th, (c) Tdmax, and (d) Tdmin. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

2) The relationship between hourly atmospheric humidity and temperature

Figure 9 shows the relationship of temperature to ea, Rh, and q under the 99.9th, 99th, 95th, 90th, and 75th percentiles. Figures 9a–c and Figs. 9d–f show the results for T and Th, respectively. The scaling behaviors of each variable are similar for T and Th, while the fluctuation in Th scaling is stronger because Th is mainly controlled by boundary layer processes and radiation (Lenderink and Meijgaard 2008). When the temperature is lower than the breaking point temperature, ea exhibits a temperature dependency similar to the 1CC rate. Rh begins to increase as T increases, then gradually stabilizes. The q exponentially increases first, then transits toward a linear increase. Overall transition temperatures (13°C for the 99.9th and 99th daily percentiles and 17°C for the 95th percentile) are in agreement with those of Th, which are approximately 11°C for the 99.9th and 99th percentiles and 13°C for the 95th percentile. This scaling behavior is weaker for lower percentiles, where the transition temperature is higher. The breaking point temperature is approximately 25°C for T and 23°C for Th. When the temperature is greater than the breaking point temperature, ea and q slightly increase or maintain stability, and Rh decreases sharply with an increase in temperature.

Fig. 9.
Fig. 9.

Relationships among temperature, hourly precipitation, and humidity for (left) T and (right) Th, with (a),(d) ea, (b),(e) Rh, and (c),(f) q. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line (black) represents the breaking point temperature, and the dash–dot line (gray) represents temperature that increases the rates of 99.9th and 99th percentile precipitation values that transitioned from 1CC to 2CC.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

Hourly intensity is binned using hourly q data, and the width of each bin is 2 g kg−1. The q and T effect at the 99.9th, 99th, 95th, 90th, and 75th percentiles on Ih are compared in Fig. 10. The two lines under the same percentile are very similar and have the same trend.

Fig. 10.
Fig. 10.

Different percentiles of Ih vs q and T.

Citation: Journal of Climate 31, 21; 10.1175/JCLI-D-18-0050.1

Both daily and hourly results showed that temperature changes in water vapor–related variables, precipitation, ea, q, Rh, and Epan are similar, especially the breaking points. These variables also have a very strong basis for observations. When the temperature is lower, ea is strictly controlled by the CC curve; Rh increases first, then stabilizes; precipitation extremes increase continuously and exhibit a positive dependence on ea or q. In this situation, ea is approximately equal to es, and precipitation extremes result from temperatures with sufficient moisture capacity. However, for higher temperatures, ea slightly decreases or remains stable and Rh sharply decreases, which leads to larger vapor pressure deficits, and precipitation extremes sharply decrease. In this situation, precipitation extremes result from high temperatures with insufficient moisture capacity. Both daily and hourly precipitation extremes result from humidity control at the same temperature. It should be pointed out that sufficient moisture capacity and insufficient moisture capacity are both at an atmospheric temperature of 2 m, which is different from the temperature at the height of clouds.

4. Conclusions

Precipitation extreme events become more intense in a warmer climate. Based on data for 16 325 552 days at 702 meteorological stations over China during 1951–2014, we find that when the temperature is lower than 25°C, precipitation extremes exhibit a temperature dependency similar to the 1CC relation, which decreases sharply with temperatures greater than 25°C. A similar scaling behavior is obtained in 10 river basins over China, where the temperature breaking point gradually increases from northwest (17°C) to southeast (25°C). This behavior suggests that the breaking point temperature depends on water vapor availability for the occurrence of precipitation and on the basin distance from the coast.

Based on data for 592 841 h at eight meteorological stations over China during 2000–15, we find that the breaking point temperature is also 25°C. When the temperature is lower than 13°C, precipitation extremes increase at a rate similar to the 1CC relation; this rate increases to a 2CC relation for 13°–25°C temperatures, then scaling decreases sharply with temperatures greater than 25°C.

We use the same scaling method to analyze the dependence of daily and hourly atmospheric humidity on temperature by analyzing relationships among Rh, ea, q, and Epan. We find that both daily and hourly temperature changes for extremes of ea, q, Rh, and Epan are accordant with that of precipitation extremes. Then we use the same method to analyze the daily and hourly precipitation extremes on atmospheric humidity by analyzing relationships between Id and ea and Ih and q. When ea is lower than the breaking point, Id exhibits a positive linear dependence on ea, the correlation coefficient is not less than 0.98, and its corresponding breaking point temperature is 24.6°C. The q and T effect on Ih are very similar under the same percentile. Both daily and hourly precipitation extremes result from humidity control at the same temperature.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (2016YFA0602402), the National Natural Science Foundation of China (41601035), the Key Programs of the Chinese Academy of Sciences (ZDRW-ZS-2017-3-1), the Chinese Academy of Sciences (CAS) Pioneer Hundred Talents Program, the CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows, Institute of Ecology and Geography, and the Chinese Academy of Sciences. The authors thank the CMDC (http://data.cma.cn) and the CERN (http://www.cnern.org.cn), including the ASA, CWA, LCA, YGA, NMD, SPD, GGF, and QIA stations, for providing data support.

APPENDIX

The Abbreviation of Variables in the Manuscript

CC

Clausius–Clapeyron

ea

Actual vapor pressure (hPa)

es

Saturated vapor pressure (hPa)

Epan

Pan evaporation (mm day−1)

Id

Daily precipitation intensity (mm day−1)

Ih

Hourly precipitation intensity (mm day−1)

q

Specific humidity (g kg−1)

Rh

Relative humidity (%)

T

Daily mean temperature at 2-m height (°C)

Th

Hourly mean temperature at 2-m height (°C)

Tdmax

Daily maximum air temperature at 2-m height in a day (2000–2000 Chinese standard time; °C)

Tdmin

Daily minimum air temperature at 2-m height in a day (2000–2000 Chinese standard time; °C)

REFERENCES

  • Allan, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp.

  • Allen, R. J., and R. Luptowitz, 2017: El Niño-like teleconnection increases California precipitation in response to warming. Nat. Commun., 8, 16055, https://doi.org/10.1038/ncomms16055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 14811484, https://doi.org/10.1126/science.1160787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bao, J., S. C. Sherwood, L. V. Alexander, and J. P. Evans, 2017: Future increases in extreme precipitation exceed observed scaling rates. Nat. Climate Change, 7, 128132, https://doi.org/10.1038/nclimate3201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barbero, R., S. Westra, G. Lenderink, and H. J. Fowler, 2017: Temperature‐extreme precipitation scaling: A two‐way causality? Int. J. Climatol., 38, 12741279, https://doi.org/10.1002/joc.5370.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, P., C. Moseley, and J. O. Haerter, 2013: Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci., 6, 181185, https://doi.org/10.1038/ngeo1731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Busuioc, A., M. Baciu, T. Breza, A. Dumitrescu, C. Stoica, and N. Baghina, 2017: Changes in intensity of high temporal resolution precipitation extremes in Romania: Implications of Clausius-Clapeyron scaling. Climate Res., 72, 239249, https://doi.org/10.3354/cr01469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. C., E. J. Kendon, N. M. Roberts, H. J. Fowler, and S. Blenkinsop, 2016: Downturn in scaling of UK extreme rainfall with temperature for future hottest days. Nat. Geosci., 9, 2428, https://doi.org/10.1038/ngeo2596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., Y. Ming, N. D. Singer, and J. Lu, 2011: Testing the Clausius‐Clapeyron constraint on the aerosol‐induced changes in mean and extreme precipitation. Geophys. Res. Lett., 38, L04807, https://doi.org/10.1029/2010GL046435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CMDC, 2012: Dataset of daily climate data from Chinese surface stations for global exchange, version 3.0. CMDC, accessed 4 August 2012, http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html.

  • Dalton, J., 1802: Experimental essays on the constitution of mixed gases: On the force of steam or vapor from water or other liquids in different temperatures, both in a Torricelli vacuum and in air, on evaporation, and on expansion of gases by heat. Manchester Lit. Philos. Soc., 5, 536602.

    • Search Google Scholar
    • Export Citation
  • Donat, M. G., A. L. Lowry, L. V. Alexander, P. A. O’Gorman, and N. Maher, 2016: More extreme precipitation in the world’s dry and wet regions. Nat. Climate Change, 6, 508513, https://doi.org/10.1038/nclimate2941; Corrigendum, 7, 154–158, https://doi.org/10.1038/nclimate3160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drobinski, P., B. Alonzo, S. Bastin, N. Da Silva, and C. Muller, 2016: Scaling of precipitation extremes with temperature in the French Mediterranean region: What explains the hook shape? J. Geophys. Res. Atmos., 121, 31003119, https://doi.org/10.1002/2015JD023497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., and R. Knutti, 2016: Observed heavy precipitation increase confirms theory and early models. Nat. Climate Change, 6, 986992, https://doi.org/10.1038/nclimate3110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goff, J. A., 1957: Saturation pressure of water on the new Kelvin temperature scale. Transactions of the American Society of Heating and Ventilating Engineers, Murray Bay, Quebec, Canada, American Society of Heating and Ventilating Engineers, 347–354.

  • Goff, J. A., and S. Gratch, 1946: Low-pressure properties of water from −160 to 212°F. Transactions of the American Society of Heating and Ventilating Engineers, New York, NY, American Society of Heating and Ventilating Engineers, 95–122.

  • Haerter, J. O., and P. Berg, 2009: Unexpected rise in extreme precipitation caused by a shift in rain type? Nat. Geosci., 2, 372373, https://doi.org/10.1038/ngeo523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, D., Y. Peiwen, L. Gaoping, and Z. Jian, 2017: Relationship between precipitation extremes with temperature in the warm season in Anhui Province (in Chinese). Climatic Environ. Res., 22, 623632.

    • Search Google Scholar
    • Export Citation
  • Jones, R. H., S. Westra, and A. Sharma, 2010: Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity. Geophys. Res. Lett., 37, L22805, https://doi.org/10.1029/2010GL045081.

    • Search Google Scholar
    • Export Citation
  • Lenderink, G., and E. V. Meijgaard, 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1, 511514, https://doi.org/10.1038/ngeo262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, S. C., C. Fu, C.-J. Shiu, J.-P. Chen, and F. Wu, 2009: Temperature dependence of global precipitation extremes. Geophys. Res. Lett., 36, L17702, https://doi.org/10.1029/2009GL040218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., X. Zhang, S. Chen, and H. Sun, 2016: Changes in water use efficiency and water footprint in grain production over the past 35 years: A case study in the North China Plain. J. Cleaner Prod., 116, 7179, https://doi.org/10.1016/j.jclepro.2016.01.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meredith, E. P., V. A. Semenov, D. Maraun, W. Park, and A. V. Chernokulsky, 2015: Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme. Nat. Geosci., 8, 615620, https://doi.org/10.1038/ngeo2483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378381, https://doi.org/10.1038/nature09763; Corrigendum, 498, 526, https://doi.org/10.1038/nature12197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molnar, P., S. Fatichi, L. Gaál, J. Szolgay, and P. Burlando, 2015: Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. Hydrol. Earth Syst. Sci., 19, 17531766, https://doi.org/10.5194/hess-19-1753-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moseley, C., C. Hohenegger, P. Berg, and J. O. Haerter, 2016: Intensification of convective extremes driven by cloud–cloud interaction. Nat. Geosci., 9, 748754, https://doi.org/10.1038/ngeo2789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, I. H., and S. K. Min, 2017: Role of convective precipitation in the relationship between subdaily extreme precipitation and temperature. J. Climate., 30, 95279537, https://doi.org/10.1175/JCLI-D-17-0075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeer, K., and G. Kirchengast, 2018: Sensitivity of extreme precipitation to temperature: The variability of scaling factors from a regional to local perspective. Climate Dyn., 11–12, 39813994, https://doi.org/10.1007/s00382-017-3857-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, M. S., and P. A. O’Gorman, 2014: Influence of microphysics on the scaling of precipitation extremes with temperature. Geophys. Res. Lett., 41, 60376044, https://doi.org/10.1002/2014GL061222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singleton, A., and R. Toumi, 2013: Super‐Clausius–Clapeyron scaling of rainfall in a model squall line. Quart. J. Roy. Meteor. Soc., 139, 334339, https://doi.org/10.1002/qj.1919.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, W., J. Li, and R. Yu, 2013: Corresponding relation between warm season precipitation extremes and surface air temperature in South China. Adv. Climate Change Res., 4, 160165, https://doi.org/10.3724/SP.J.1248.2013.160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, W., W. Yuan, J. Li, and R. Yu, 2015: Correlation between peak intensity of extreme short-duration rainfall and humidity and surface air temperature in southeast coast of China (in Chinese). J. Trop. Meteor., 21, 276284, https://doi.org/10.16555/j.1006-8775.2015.03.007.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051217, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki, 2011: Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708, https://doi.org/10.1029/2011GL048426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G., D. Wang, K. E. Trenberth, A. Erfanian, M. Yu, M. G. Bosilovich, and D. T. Parr, 2017: The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat. Climate Change, 7, 268274, https://doi.org/10.1038/nclimate3239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., X. Zhang, X. Zhang, L. Shao, S. Chen, and X. Liu, 2016: Soil water regime affecting correlation of carbon isotope discrimination with yield and water-use efficiency of winter wheat. Crop Sci., 56, 760772, https://doi.org/10.2135/cropsci2014.11.0793.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westra, S., and Coauthors, 2014: Future changes to the intensity and frequency of short‐duration extreme rainfall. Rev. Geophys., 52, 522555, https://doi.org/10.1002/2014RG000464.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, C., P. Wu, L. Zhang, and L. Song, 2016: Robust increase in extreme summer rainfall intensity during the past four decades observed in China. Sci. Rep., 6, 38506, https://doi.org/10.1038/srep38506.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, H., E. J. Fetzer, S. Wong, A. Behrangi, D. Yang, and B. H. Lambrigtson, 2015: Increasing atmospheric water vapor and higher daily precipitation intensity over northern Eurasia. Geophys. Res. Lett., 42, 94049410, https://doi.org/10.1002/2015GL066104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, D.-H., X.-R. Li, F. Zhang, Z.-S. Zhang, and Y.-L. Chen, 2016: Effects of rainfall intensity and intermittency on woody vegetation cover and deep soil moisture in dryland ecosystems. J. Hydrol., 543B, 270282, https://doi.org/10.1016/j.jhydrol.2016.10.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., F. W. Zwiers, G. Li, H. Wan, and A. J. Cannon, 2017: Complexity in estimating past and future extreme short-duration rainfall. Nat. Geosci., 10, 255259, https://doi.org/10.1038/ngeo2911.

    • Crossref
    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • Allan, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp.

  • Allen, R. J., and R. Luptowitz, 2017: El Niño-like teleconnection increases California precipitation in response to warming. Nat. Commun., 8, 16055, https://doi.org/10.1038/ncomms16055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 14811484, https://doi.org/10.1126/science.1160787.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bao, J., S. C. Sherwood, L. V. Alexander, and J. P. Evans, 2017: Future increases in extreme precipitation exceed observed scaling rates. Nat. Climate Change, 7, 128132, https://doi.org/10.1038/nclimate3201.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barbero, R., S. Westra, G. Lenderink, and H. J. Fowler, 2017: Temperature‐extreme precipitation scaling: A two‐way causality? Int. J. Climatol., 38, 12741279, https://doi.org/10.1002/joc.5370.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, P., C. Moseley, and J. O. Haerter, 2013: Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci., 6, 181185, https://doi.org/10.1038/ngeo1731.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Busuioc, A., M. Baciu, T. Breza, A. Dumitrescu, C. Stoica, and N. Baghina, 2017: Changes in intensity of high temporal resolution precipitation extremes in Romania: Implications of Clausius-Clapeyron scaling. Climate Res., 72, 239249, https://doi.org/10.3354/cr01469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. C., E. J. Kendon, N. M. Roberts, H. J. Fowler, and S. Blenkinsop, 2016: Downturn in scaling of UK extreme rainfall with temperature for future hottest days. Nat. Geosci., 9, 2428, https://doi.org/10.1038/ngeo2596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., Y. Ming, N. D. Singer, and J. Lu, 2011: Testing the Clausius‐Clapeyron constraint on the aerosol‐induced changes in mean and extreme precipitation. Geophys. Res. Lett., 38, L04807, https://doi.org/10.1029/2010GL046435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CMDC, 2012: Dataset of daily climate data from Chinese surface stations for global exchange, version 3.0. CMDC, accessed 4 August 2012, http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html.

  • Dalton, J., 1802: Experimental essays on the constitution of mixed gases: On the force of steam or vapor from water or other liquids in different temperatures, both in a Torricelli vacuum and in air, on evaporation, and on expansion of gases by heat. Manchester Lit. Philos. Soc., 5, 536602.

    • Search Google Scholar
    • Export Citation
  • Donat, M. G., A. L. Lowry, L. V. Alexander, P. A. O’Gorman, and N. Maher, 2016: More extreme precipitation in the world’s dry and wet regions. Nat. Climate Change, 6, 508513, https://doi.org/10.1038/nclimate2941; Corrigendum, 7, 154–158, https://doi.org/10.1038/nclimate3160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drobinski, P., B. Alonzo, S. Bastin, N. Da Silva, and C. Muller, 2016: Scaling of precipitation extremes with temperature in the French Mediterranean region: What explains the hook shape? J. Geophys. Res. Atmos., 121, 31003119, https://doi.org/10.1002/2015JD023497.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., and R. Knutti, 2016: Observed heavy precipitation increase confirms theory and early models. Nat. Climate Change, 6, 986992, https://doi.org/10.1038/nclimate3110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goff, J. A., 1957: Saturation pressure of water on the new Kelvin temperature scale. Transactions of the American Society of Heating and Ventilating Engineers, Murray Bay, Quebec, Canada, American Society of Heating and Ventilating Engineers, 347–354.

  • Goff, J. A., and S. Gratch, 1946: Low-pressure properties of water from −160 to 212°F. Transactions of the American Society of Heating and Ventilating Engineers, New York, NY, American Society of Heating and Ventilating Engineers, 95–122.

  • Haerter, J. O., and P. Berg, 2009: Unexpected rise in extreme precipitation caused by a shift in rain type? Nat. Geosci., 2, 372373, https://doi.org/10.1038/ngeo523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, D., Y. Peiwen, L. Gaoping, and Z. Jian, 2017: Relationship between precipitation extremes with temperature in the warm season in Anhui Province (in Chinese). Climatic Environ. Res., 22, 623632.

    • Search Google Scholar
    • Export Citation
  • Jones, R. H., S. Westra, and A. Sharma, 2010: Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity. Geophys. Res. Lett., 37, L22805, https://doi.org/10.1029/2010GL045081.

    • Search Google Scholar
    • Export Citation
  • Lenderink, G., and E. V. Meijgaard, 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1, 511514, https://doi.org/10.1038/ngeo262.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, S. C., C. Fu, C.-J. Shiu, J.-P. Chen, and F. Wu, 2009: Temperature dependence of global precipitation extremes. Geophys. Res. Lett., 36, L17702, https://doi.org/10.1029/2009GL040218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., X. Zhang, S. Chen, and H. Sun, 2016: Changes in water use efficiency and water footprint in grain production over the past 35 years: A case study in the North China Plain. J. Cleaner Prod., 116, 7179, https://doi.org/10.1016/j.jclepro.2016.01.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meredith, E. P., V. A. Semenov, D. Maraun, W. Park, and A. V. Chernokulsky, 2015: Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme. Nat. Geosci., 8, 615620, https://doi.org/10.1038/ngeo2483.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378381, https://doi.org/10.1038/nature09763; Corrigendum, 498, 526, https://doi.org/10.1038/nature12197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molnar, P., S. Fatichi, L. Gaál, J. Szolgay, and P. Burlando, 2015: Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. Hydrol. Earth Syst. Sci., 19, 17531766, https://doi.org/10.5194/hess-19-1753-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moseley, C., C. Hohenegger, P. Berg, and J. O. Haerter, 2016: Intensification of convective extremes driven by cloud–cloud interaction. Nat. Geosci., 9, 748754, https://doi.org/10.1038/ngeo2789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, I. H., and S. K. Min, 2017: Role of convective precipitation in the relationship between subdaily extreme precipitation and temperature. J. Climate., 30, 95279537, https://doi.org/10.1175/JCLI-D-17-0075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schroeer, K., and G. Kirchengast, 2018: Sensitivity of extreme precipitation to temperature: The variability of scaling factors from a regional to local perspective. Climate Dyn., 11–12, 39813994, https://doi.org/10.1007/s00382-017-3857-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, M. S., and P. A. O’Gorman, 2014: Influence of microphysics on the scaling of precipitation extremes with temperature. Geophys. Res. Lett., 41, 60376044, https://doi.org/10.1002/2014GL061222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singleton, A., and R. Toumi, 2013: Super‐Clausius–Clapeyron scaling of rainfall in a model squall line. Quart. J. Roy. Meteor. Soc., 139, 334339, https://doi.org/10.1002/qj.1919.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, W., J. Li, and R. Yu, 2013: Corresponding relation between warm season precipitation extremes and surface air temperature in South China. Adv. Climate Change Res., 4, 160165, https://doi.org/10.3724/SP.J.1248.2013.160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, W., W. Yuan, J. Li, and R. Yu, 2015: Correlation between peak intensity of extreme short-duration rainfall and humidity and surface air temperature in southeast coast of China (in Chinese). J. Trop. Meteor., 21, 276284, https://doi.org/10.16555/j.1006-8775.2015.03.007.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051217, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki, 2011: Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708, https://doi.org/10.1029/2011GL048426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G., D. Wang, K. E. Trenberth, A. Erfanian, M. Yu, M. G. Bosilovich, and D. T. Parr, 2017: The peak structure and future changes of the relationships between extreme precipitation and temperature. Nat. Climate Change, 7, 268274, https://doi.org/10.1038/nclimate3239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., X. Zhang, X. Zhang, L. Shao, S. Chen, and X. Liu, 2016: Soil water regime affecting correlation of carbon isotope discrimination with yield and water-use efficiency of winter wheat. Crop Sci., 56, 760772, https://doi.org/10.2135/cropsci2014.11.0793.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westra, S., and Coauthors, 2014: Future changes to the intensity and frequency of short‐duration extreme rainfall. Rev. Geophys., 52, 522555, https://doi.org/10.1002/2014RG000464.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, C., P. Wu, L. Zhang, and L. Song, 2016: Robust increase in extreme summer rainfall intensity during the past four decades observed in China. Sci. Rep., 6, 38506, https://doi.org/10.1038/srep38506.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, H., E. J. Fetzer, S. Wong, A. Behrangi, D. Yang, and B. H. Lambrigtson, 2015: Increasing atmospheric water vapor and higher daily precipitation intensity over northern Eurasia. Geophys. Res. Lett., 42, 94049410, https://doi.org/10.1002/2015GL066104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, D.-H., X.-R. Li, F. Zhang, Z.-S. Zhang, and Y.-L. Chen, 2016: Effects of rainfall intensity and intermittency on woody vegetation cover and deep soil moisture in dryland ecosystems. J. Hydrol., 543B, 270282, https://doi.org/10.1016/j.jhydrol.2016.10.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., F. W. Zwiers, G. Li, H. Wan, and A. J. Cannon, 2017: Complexity in estimating past and future extreme short-duration rainfall. Nat. Geosci., 10, 255259, https://doi.org/10.1038/ngeo2911.

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  • Fig. 1.

    The locations of meteorological stations. (a) Daily meteorological stations and 10 river basins over China. (b) Hourly meteorological stations, including ASA, CWA, LCA, YGA, NMD, SPD, GGF, and QIA.

  • Fig. 2.

    Daily precipitation intensity Id dependence on T. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

  • Fig. 3.

    (a)–(j) Daily precipitation intensity dependence on temperature over 10 river basins in China. (k) The breaking point temperatures of the 10 river basins. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

  • Fig. 4.

    Temperature relationships with daily precipitation, humidity, and evaporation: (a) ea, (b) Rh, and (c) Epan. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line represents the breaking point temperature.

  • Fig. 5.

    Percentiles for Epan vs vapor pressure deficit. The vapor pressure deficit is the difference between es and ea. Scatterplots represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Solid lines and equations explain the trend lines and fitted formulas of the scatterplots.

  • Fig. 6.

    (a)–(j) The 99.9th percentile for Id, ea, Rh, and Epan in 10 river basins. (k) The breaking point temperatures for the 10 river basins. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line represents the breaking point temperature.

  • Fig. 7.

    Daily precipitation intensity Id dependence on ea. (a) Different percentiles of Id vs ea (solid color lines) and es (dash-dot-dot color lines). The medium gray dash line is the es value at a given T. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. (b) Different percentiles of Id vs ea when ea is lower than the breaking point. The color equations explain the trend lines and fitted formulas of the corresponding color scatterplots.

  • Fig. 8.

    Hourly precipitation intensity dependence on temperature. (a) Observed 1-h precipitation intensity vs T, (b) Th, (c) Tdmax, and (d) Tdmin. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 1CC (black) and 2CC (red) relations.

  • Fig. 9.

    Relationships among temperature, hourly precipitation, and humidity for (left) T and (right) Th, with (a),(d) ea, (b),(e) Rh, and (c),(f) q. Solid color lines represent the 99.9th, 99th, 95th, 90th, and 75th percentiles. Gray bands represent the 90th percentile confidence intervals estimated by the bootstrap method. Dotted lines represent the 0.5CC (gray), 1CC (black), and 2CC (red) relations. The dash–dot line (black) represents the breaking point temperature, and the dash–dot line (gray) represents temperature that increases the rates of 99.9th and 99th percentile precipitation values that transitioned from 1CC to 2CC.

  • Fig. 10.

    Different percentiles of Ih vs q and T.

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