1. Introduction
The change in global precipitation during the past half century is unlike widespread temperature increases (Wang and Ding 2006; Swanson et al. 2009; Sun et al. 2012), and precipitation changes show significant spatial and temporal differences (Sun et al. 2018, 2010). The spatial variability is demonstrated as precipitation variation from coastal to inland China; temporal variability is mainly expressed as daily, seasonal, and interannual variabilities (Shige et al. 2017; Giles et al. 2020). The diurnal cycle of precipitation (DC) is an important indicator for diagnosing the impact of global warming on the environment. For instance, the increase in flash floods has been closely related to the rise in heavy diurnal precipitation in recent decades (Ashley and Ashley 2008; Hanel et al. 2016). There is an increasing trend in diurnal precipitation intensities, which contributes to the total precipitation amounts (Chan et al. 2014; Hanel et al. 2016). Currently, long time series records of high temporal resolution (semidiurnal or hourly) observations are the primary limiting factor for quantitative changes in the diurnal differences of precipitation.
The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6) indicated that the in situ observation data and remote sensing precipitation products, for example, the Global Precipitation Measurement (GPM) dataset, the Tropical Rainfall Measuring Mission (TRMM), the Global Satellite Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), are essential for gaining insight into the variability of the diurnal cycle of precipitation (IPCC 2022). Global and regional climate models (GCMs and RCMs, respectively) are also provided with subdaily-scale precipitation products, while most models have difficulty simulating the diurnal differences of precipitation over land (Couvreux et al. 2015). This is because models simulate the peak in the diurnal differences of precipitation much earlier than in situ observations. Covey et al. (2016) pointed out that subgrid-scale physics causes this error. IPCC AR6 reported that high-resolution models are being developed to improve the model’s ability to simulate the diurnal differences of precipitation.
Daily precipitation intensities have increased in the midlatitudes of the Northern Hemisphere during the twentieth century under global warming (Alexander et al. 2006; Chan et al. 2014; Cheng et al. 2019; Schroeer et al. 2018; Westra et al. 2013). There is a consensus that increasing the near-surface temperature increases the extremes of daily precipitation (Westra et al. 2013). Daily precipitation intensity has increased by 5.2% for each degree of increase in the mean global temperature (Zhang et al. 2013). Increased greenhouse gases are known to be an important driving factor for the increase in daily precipitation extremes (Zhang et al. 2017). In China, the human contribution to changes in daily precipitation extremes is thought to be 13% with 90% confidence (Chen and Sun 2017). Simultaneously, the increase in daily precipitation extremes has resulted in frequent flash flood events in recent decades (Ashley and Ashley 2008; Hanel et al. 2016). Consequently, increased knowledge of the changes in the diurnal cycle of precipitation will lead to a better understanding of their environmental effects.
Significant regional differences in diurnal precipitation changes have been observed in China over the past half century. There are various control mechanisms of the diurnal precipitation in different zones, such as the precipitation type (Yu et al. 2010), thermal circulations associated with topography (Sun et al. 2012), sea breeze (Jiang et al. 2017), oscillations in boundary layer stability (Pan and Chen 2019), and anthropogenic influences (Ma et al. 2017). The summer diurnal precipitation in southern and northeastern China peaks in the afternoon, while that in most of the Tibetan Plateau peaks at midnight (Yu et al. 2007). Diurnal precipitation tends to peak in the early morning in winter, whereas in other seasons, it tends to peak in the late afternoon in southwest China (Huang and Chan 2012). The trend of increasing precipitation is more robust at night than in the day in northwest China (Han et al. 2014). In general, the annual precipitation amounts, and frequency of nocturnal precipitation are more significant than daytime precipitation in Xinjiang (Han et al. 2014). Precipitation over southern China exhibits two distinct diurnal phases: late night [2200–0600 China standard time (CST)] and late afternoon (1400–2200 CST) maxima, depending on the location, precipitation type, and duration (Yu et al. 2010). Intense diurnal thermal circulations caused by the topography (named mountain–plains solenoid) and boundary layer inertial oscillation can increase the nocturnal rainfall in the North China plains, but there are substantial regional differences (Pan and Chen 2019). However, previous studies mainly focused on the peaks and influencing factors of the subdaily precipitation, and few have focused on the trend and intensity of the diurnal differences of precipitation.
Therefore, this study uses in situ observations of semidiurnal precipitation data to analyze the changing characteristics of the diurnal differences of precipitation in China since 1990 and the possible influencing factors. We define a precipitation index (Z index) that focuses on 1) characterizing variations in the diurnal differences of precipitation in China and 2) quantifying the intensity of the diurnal differences of precipitation. Section 2 describes the study area, data, and methods used. We examined the temporal and spatial contrasts in the diurnal differences of precipitation (section 3). We then define the Z index to describe the diurnal differences of precipitation changes (section 4). The broader implications of these findings are discussed in section 5, and section 6 summarizes our main results.
2. Data and methods
a. Data sources
Data from 698 meteorological stations across China (Fig. 1) were collected from the meteorological data-sharing network of the China Meteorological Data Service Center (CMDC; http://data.cma.cn/), with a semidiurnal (12 h) period for the complete record from January 1990 to December 2019. Precipitation includes solid precipitation (snow), liquid precipitation (rain), and a mixture (sleet). The nocturnal (2000–0800 CST) and daytime (0800–2000 CST) precipitation totals were aggregated for each station. Daily precipitation is the sum of nocturnal and daytime precipitation. Observational data have been subject to quality control by the National Meteorological Information Center (NMIC; Feng et al. 2004). Meanwhile, the winter season is from December to February (DJF), spring is from March to May (MAM), summer is from June to August (JJA), and autumn is from September to November (SON). The entire study area includes nine river basins (Fig. 1): Continental basin, Haihe River basin, Huaihe River basin, Pearl River basin, Songhua–Liaohe River basin, Southeast basin, Southwest basin, Yangtze River basin, and Yellow River basin; the location of the basins is shown in Fig. 1.
The study area showing the location of the 698 stations from the China Meteorological Administration and the nine river basins. The red triangle symbol represents meteorological stations with a complete semidiurnal precipitation (mm) record from January 1990 to December 2019. The shape file was provided by RESDC (http://www.resdc.cn).
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
The East Asian summer monsoon (EASM) index (1990–2019) was collected from the national Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/zh-hans/), and the EASM index is an essential tool for studying the East Asian summer monsoon (Zhao et al. 2015).
b. Method
The Mann–Kendall nonparametric trend test (Hirsch and Slack 1984; Hamed 2008) was used to assess the trend significance for daytime and nocturnal precipitation, the Z index during 1990–2019, and to conduct significance tests. The slope of the trend was estimated using Sen’s nonparametric trend estimator (Sen 1968).
The topographic exposure of all stations was calculated based on the 250-m DEM data using neighborhood analysis of the spatial analyst tools in ArcGIS 10.5. Mean elevation values are calculated for a 10-km rectangular neighborhood radius (ring surrounding the pixel representing the station). This is then subtracted from the original pixel elevation to create an elevation difference. Positive/negative elevation differences (exposure) represent ridge/valley stations. The DEM data source was provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC; http://www.resdc.cn).
3. Results
a. Broad temporal changes of daytime and nocturnal precipitation across China
Annual daily precipitation in China averages approximately 800 mm, and daytime and nocturnal contributions are both approximately 400 mm on average, although nocturnal precipitation is consistently marginally higher at the countrywide scale (Fig. 2a). The seasonal breakdown (Fig. 2b) shows that precipitation is mainly concentrated in summer (approximately 400 mm), accounting for about half of the annual total, followed by spring and autumn. Figure 2b also shows that nocturnal precipitation is higher than daytime precipitation in autumn, winter, and spring; however, the opposite is true in summer. In terms of the monthly totals, the full range of daytime precipitation in June, July, and August was higher than that at night, whereas all other months showed the opposite trend (Fig. 2c). Furthermore, the interquartile range (IQR; IQR = Q3 − Q1, where Q3 and Q1 are upper quartile and lower quartile, respectively) shows monthly nocturnal and daytime precipitation variations, which vary slightly in the winter half of the year and vary considerably in the summer half (Fig. 2c). Meanwhile, there are many extreme values of nocturnal and daytime precipitation between May and October, which may be affected by the summer monsoon and tropical cyclone.
Basic information on the precipitation in China over the past 30 years: (a) daily daytime and nocturnal precipitation in China, (b) mean value of seasonal daytime and nocturnal precipitation, and (c) year-to-year variability in the monthly mean daytime and nocturnal precipitation and boxplot of the multimonthly daytime and nocturnal precipitation. The boxes indicate the IQR, the whiskers show the full range of the data, and the red plus signs show the extreme values.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
There were relatively stable changes in the annual daily, daytime, and nocturnal precipitation in China from 1990 to 2019. Although the variations in annual precipitation in China are relatively stable, there have been large regional differences since 1990 (Fig. 3). Most show an increasing trend, but only the Continental basin (0.80 mm yr−1; p < 0.05) and Yellow River basin (3.10 mm yr−1; p < 0.01) pass the significance test. The Haihe River basin, Huaihe River basin, and Southwest basin show decreasing trends, but only the Southwest basin (−2.78 mm yr−1) passes the significance test at the 0.1 level.
Annual precipitation trends for the nine river basins in China over the past 30 years: (a) Continental basin, (b) Haihe River basin, (c) Huaihe River basin, (d) Pearl River basin, (e) Songhua–Liaohe River basin, (f) Southeast basin, (g) Southwest basin, (h) Yangtze River basin, and (i) Yellow River basin. The locations of the nine river basins are shown in Fig. 1.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
The seasonal distribution of daytime and nocturnal precipitation in the nine river basins showed that summer (JJA) precipitation was the highest, followed by spring (MAM), autumn (SON), and winter (DJF). Meanwhile, differences between the daytime and nocturnal precipitation in summer were greater than those in other seasons (Table 1). Despite some consistent differences between daytime and nocturnal precipitation, there is a common interannual variability in most basins. Daytime precipitation is noticeably higher in the Pearl River basin, Songhua–Liaohe River basin, and Southeast basin, whereas in the Southwest basin, nocturnal precipitation is significantly higher.
The daytime and nocturnal precipitation (mm) of nine river basins in China from 1990 to 2019. The names of the river basins numbered from 1 to 9 are the Continental basin, Haihe River basin, Huaihe River basin, Pearl River basin, Songhua–Liaohe River basin, Southeast basin, Southwest basin, Yangtze River basin, and Yellow River basin, respectively.
The relationship analysis between the distance of stations from the coast and the Z index. The comparison with stations ≤ 100 km and those > 100 but ≤300 km are shown. The Pearson’s method was used to detect the correlation coefficient. The Z index (%) represents the mean value of the Z index, and n represent the number of stations. One, two, and three asterisks indicate p < 0.05, p < 0.01, and p < 0.001, respectively.
b. Spatial patterns of daytime and nocturnal precipitation trends
In winter, daytime precipitation increased in the Continental basin, Songhua–Liaohe River basin, and Southwest basin (Fig. 4a), while it decreased in the Haihe River basin, Huaihe River basin, Pearl River basin, Southeast basin, and Yangtze River basin. Figure 4b shows that most stations experienced an increase in the spring daytime precipitation, whereas it decreased mainly in southeastern China. In summer, the daytime precipitation showed more spatial heterogeneity than in other seasons (Fig. 4c). In autumn, the daytime precipitation increased in most river basins (Fig. 4d) and decreased only in the Songhua–Liaohe River basin and Southwest basin.
Spatial patterns of the seasonal daytime precipitation trends in China from 1990 to 2019: (a) winter (DJF), (b) spring (MAM), (c) summer (JJA), and (d) autumn (SON). A black dot represents significance at p < 0.1.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
The spatial patterns of variations in the annual nocturnal and daytime precipitation are broadly similar, but there are a few areas that showed differences. Figure 5 shows that the seasonal spatial patterns of nocturnal precipitation changes are broadly similar to the daytime precipitation changes, except in the Haihe River basin. Winter nocturnal precipitation increased in the Haihe River basin (Fig. 5a), but daytime precipitation decreased (Fig. 4a). Additionally, the summer nocturnal precipitation has decreased in the central part of the Yangtze River basin, and the upper stream of the Pearl River basin (Fig. 5c) is more marked than the equivalent decrease in the daytime (Fig. 4c).
As in Fig. 4, but for nighttime precipitation.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
c. Quantifying the changes in the diurnal differences of precipitation
1) Definition of the diurnal differences Z index
To better understand the contrasts between daytime and nocturnal precipitation, we evaluated the differences between the climatological daytime and nocturnal precipitation at each station using Student’s t test. The t test results indicated that two-thirds of the stations had significant differences between daytime and nocturnal precipitation (Fig. S1 in the online supplemental material), which includes most of western, central, and southern China. Some parts of eastern China plain did not show significant differences. We defined a precipitation index (Z index) based on Eq. (1) to define the diurnal differences of precipitation at the station scale in China.
2) Long-term trends in the diurnal differences of precipitation
Figure 6a1 shows that in most regions, there is a small annual difference between the daytime and nocturnal precipitation, with the Z index ranging from −16% to 16%. There are two exceptions: southwestern China (below −16%) and southeastern China (over 16%). Meanwhile, seasonal variations in the Z index were broadly similar to the annual pattern (Figs. 6b1–e1). In southwestern China, the Z index falls below −16%, especially in winter (Fig. 6b1) and spring (Fig. 6c1). In southeastern China, the Z index rises over 16%, mainly in the summer (Fig. 6d1). Thus, the nighttime precipitation regime in southwestern China lasts most of the year, whereas the daytime-dominated regime is most robust in the summer in southeastern China and largely disappears in winter. Therefore, more stations show a nighttime- or daytime-dominated regime in winter and summer, respectively.
Annual and seasonal Z-index trends across China from 1990 to 2019: (a) annual, (b) winter, (c) spring, (d) summer, and (e) autumn for the (left) mean Z index, (center) Z-index trend, and (right) time series of Z index for the entirety of China.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
An analysis of the spatial pattern of trends in the Z index shows that variations on an annual scale are relatively small (between −0.5% and 0.5% yr−1; Fig. 6a2). The annual mean Z index of China is about −2%, and its long-term change increases at a rate of 0.06% yr−1 (p < 0.1) (Fig. 6a3). This implies that the intensity of the diurnal differences of precipitation has decreased in China since 1990 according to Eq. (1). The seasonal spatial pattern of trends in the Z index is similar (Figs. 6b2–e2), with a predominant increase and a sporadic distribution of decreasing stations. The seasonal mean Z index averaged over China was negative (Figs. 6b3–e3), in addition to the summer (approximately 2.5%). The winter increase was most pronounced with an increase of 0.12% yr−1 (p < 0.1), and this also extended to spring (0.04% yr−1; insignificant); summer and autumn showed the same increase rate of 0.08% yr−1 (p < 0.05). Therefore, an increase in the mean Z index indicates that the intensity of the diurnal differences of precipitation has reduced in China since 1990.
The regional scale of the mean Z index is negative annually and seasonally for most river basins since 1990 (Fig. 7) but positive in summer (Fig. 7d). Figure 7 shows an increasing trend of the annual Z index for all river basins except for the Haihe River basin. However, all of the increase rates are less than 0.2% yr−1, which implies that the difference between daytime and nocturnal precipitation is decreasing. In winter, the most significant increase in the Z index amounting to 0.4% yr−1 occurred in the Songhua–Liaohe River basin, the Southwest River basins, and the Yellow River basin (Fig. 7b), followed by the Yangtze River basin and the Southeast River basin (0.2% yr−1). In contrast, the Huaihe River basin (approximately −0.2% yr−1) and the Haihe River basin (0.4% yr−1) decreased. In spring, the changes in the Z index were more variable and increased in the Haihe River basin, Songhua–Liaohe River basin, and Yellow River basin, but decreased in the Continental basin, Huaihe River basin, Pearl River basin, and Southeast basin. In summer and autumn, the Z index of most river basins increased, although the absolute trends were small (less than 0.2% yr−1). Overall, it appears that the annual and seasonal Z indices are increasing, representing a reduction in the intensity of the diurnal differences of precipitation in China since 1990. Further analyses below shed more light on the reasons for this decrease.
Scatterplots of the mean Z index at the river basin scales in different seasons vs trends in the Z index from 1990 to 2019. The x and y axes represent the mean Z-index value and the mean trend in Z index, respectively.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
4. Further analyses
a. Terrain effects
Local atmospheric circulation systems play a role in redistributing the water vapor on a daily scale (Christopherson and Birkeland 2015; Lorenz et al. 2019). This can occur through local atmospheric circulations related to the mountain and valley breeze system (Christopherson and Birkeland 2015) and sea-breeze effects (Jiang et al. 2017). Mountain air rapidly gains heat energy during the daytime and cools rapidly at night. This causes the valley air to rise uphill during the daytime, producing a valley breeze (Christopherson and Birkeland 2015, p. 163), concentrating precipitation over the mountain summits (in theory, this would lead to a positive Z index). During the night, this process is reversed, with cooler air subsiding downhill into the valleys and causing a mountain breeze. This process can contribute to precipitation in valleys or basins (in theory, creating a negative Z index in valley locations).
Figure 8 shows that the Z index tends to be negative in high-elevation regions (≥2000 m), especially over 3000 m, whereas in low-elevation regions (<2000 m) the Z index is more complex. However, nearly all high-elevation stations in southwestern China with low Z values are located in basins surrounded by mountains well over 3000 m and are biased toward river valley locations. We calculated the topographic exposure of all stations based on the 250-m DEM data, and the results show that 76.1% of stations (531/698) are located with negative topographic exposure (−ve; valleys), and only 21.1% of stations have positive topographic exposure (+ve; ridges); other stations (2.8%) are located in flat regions. The mean Z index from stations with +ve and −ve exposure have obvious differences in winter and summer, while in spring and autumn they are similar (Fig. 9a). In winter, the mean Z index is −4.6% and 9.4% at ridge and valley stations, respectively (the nocturnal precipitation maximum is stronger in valleys). In summer, the mean Z index is 0.9% and 2.7% at ridge and valley stations, respectively (the daytime precipitation maximum is stronger in valleys). Trends in the Z index are broadly similar for both topographic classes (Fig. 9b), except for summer. The Z index shows an increasing trend for the year and all seasons for both +ve and −ve groups, especially in winter (about 0.15% yr−1). In summer, the Z index increased by 0.15% yr−1 at ridge stations, but only by 0.04% yr−1 at valley stations.
The annually and seasonally averaged Z index of stations with station elevation. The Z index is based on Eq. (2). Shown are (a) annual, (b) winter, (c) spring, (d) summer, and (e) autumn values. The red line represents an elevation of 2000 m.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
The annual and seasonal mean Z-index changes at different topographic exposures in China from 1990 to 2019, showing (a) mean Z index and (b) Z-index trend. The x-axis ticks labeled +ve and −ve represent the positive (ridge stations) and negative topographic exposures (valley stations), respectively.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
Thus, it is suggested that the valley breeze controls the local atmospheric circulation (Christopherson and Birkeland 2015), resulting in the dominance of nocturnal precipitation over daytime precipitation at the former stations. A good example is the well-known late evening and nocturnal precipitation maximum in Lhasa. Guo et al. (2014), in an analysis across the Tibetan Plateau in summer, showed that valley sites tended to have an evening precipitation maximum, while mountain sites tended to have a late morning/early afternoon maximum, indicating the importance of local topography. Additionally, on the southern and eastern edges of the Tibetan Plateau, strong valley/slope circulations are known to initiate nocturnal convection through convergence with low-level jets, especially where slopes are steep. Cooling at high elevations is rapid after sunset (Liu et al. 2009); for example, at the Bomi station located at the southeastern edge of the Tibetan Plateau.
b. Sea-breeze effects
Meanwhile, sources of water vapor in most regions of China are controlled by monsoon systems; thus, the distance of a station from the coast impacts the diurnal cycle of precipitation, for example, through local atmospheric circulations related to sea-breeze effects (Christopherson and Birkeland 2015; Jiang et al. 2017; Lorenz et al. 2019). All stations were classified into two categories based on the distance from the coast: 1) distance ≤ 100 km includes 106 stations; 2) distance > 100 but ≤ 300 km includes 110 stations. There is a positive correlation between the Z index and distance from the coast at the first category stations, while this is negative at the second category stations (Table 2). This indicates that the station is closer to the coast, and more precipitation occurs in the daytime. The largest Z index in both the first and second categories is in summer, and the Z index of the first category station was significantly positive correlate to the distance from the coast (r = 0.39; p < 0.001), while the second category is irrelevant (Table 2), which may result from the superposition of summer monsoon and sea-breeze effects. Therefore, the sea-breeze effect is also vital in redistributing water vapor with distance from the coast on a diurnal scale.
c. Urbanization effects
Land-use changes, such as urbanization, are another factor affecting the precipitation changes (Lorenz et al. 2019; Zhang et al. 2019). Urban regions undergo urbanization owing to economic development and are subject to the UHI effect (Hao et al. 2018; Sun et al. 2016). During the day, temperatures rise more rapidly in urban areas, creating a thermal convergence zone over the urban area, enhancing the daytime precipitation. At night, the process is reversed, and precipitation mainly occurs in rural areas. However, there is a possible additional mechanism contributing to the nocturnal precipitation outside the cities in southern China, particularly in summer, and this may be due to the relationships observed in this study. During hot days, a sea breeze develops, moving cool air inland from the South China Sea during the afternoon. This creates a convergence zone parallel to the coast and enhances the afternoon precipitation in this area (Chen et al. 2015; Jiang et al. 2017). Previous studies of the diurnal cycle of precipitation in this region suggest that this may be a combination of two smaller peaks, one in the morning due to enhanced southwesterly flow, and one in the mid- to late afternoon due to land surface heating and the inland movement of the sea-breeze (Jiang et al. 2017). However, this effect is relatively weak. Therefore, land-use change from rural to urban appears to increase the difference between daytime and nocturnal precipitation in summer to some extent by enhancing the daytime maximum; however, a more detailed analysis is required.
d. East Asian summer monsoon
Changes in the daytime and nighttime precipitation are affected by local forcing and large-scale synoptic or monsoon flows. The Pearson’s method was used to detect the correlation coefficient between daytime and nocturnal and EASM (Huang and Zhao 2019) and analyzed EASM relationships with daytime and nocturnal precipitation. There were significant spatial differences in the correlations between daytime and nighttime precipitation and EASM (Fig. 10). The correlation between daytime and nighttime precipitation and EASM was significantly positive in in the Yangtze River basin, the Huaihe River basin, and the downstream region of the Yellow River basin, but not in other basins (Figs. 10a,b). Thus, the EASM has a significant effect on daytime and nighttime precipitation in the eastern monsoon region of China.
The relationship between annual precipitation and EASM across China during 1990–2019 for (a) daytime and (b) nighttime precipitation. A black dot represents significance at p < 0.05.
Citation: Journal of Applied Meteorology and Climatology 61, 8; 10.1175/JAMC-D-21-0232.1
5. Conclusions
Our study summarized the changes in the diurnal differences of precipitation over China from 1990 to 2019, based on data from 698 stations. The main results are as follows:
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There is spatial heterogeneity in daytime and nocturnal precipitation changes in China over the past 30 years. Most areas in the northwest (arid basins) and southeast (humid basins) showed increased precipitation. There is a zone from northeast to southwest across central China that has shown a decrease. The other areas were more complex. For example, the downstream part of the Yangtze River basin and Southeast basin showed increased daytime and nocturnal precipitation during winter and summer, but decreased amounts during spring and autumn. In contrast, the headwaters of the Yangtze River basin and Yellow River basin showed increased precipitation (daytime and nocturnal) during all seasons.
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The intensity of the diurnal differences of precipitation has been decreasing in China since 1990. There is a slight annual difference between daytime and nocturnal precipitation in China, with an annual mean Z index of approximately −2%. The Z index is negative in winter, spring, and autumn (nocturnal maximum), and positive in summer (daytime maximum). Over time, the Z index increased in all seasons. The increase in winter was most pronounced, with an increase of 0.12% yr−1 (p < 0.1), followed by summer (0.08% yr−1) and autumn (0.08% yr−1); although the increase in spring is 0.04% yr−1, it is insignificant. Therefore, the mean Z index is negative annually and seasonally (except for summer), with an increasing trend indicating that the intensity of the diurnal differences of precipitation has decreased in China from 1990 to 2019.
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Topographic exposure and distance from the coast play important roles in the diurnal cycle of precipitation. In winter, the mean Z index values of stations are −4.6% and −9.4% at ridge and valley sites, respectively, while in summer, they are 0.9% and 2.7% at ridge and valley sites, respectively. The first category Z index is significantly positively correlated with distance from the coast (r = 0.39; p < 0.001) in summer, in contrast, the second category is irrelevant, which may result from the superposition of the summer monsoon and sea-breeze effects.
Acknowledgments.
This work was supported by the National Natural Science Foundation of China (Grants 41807159, 41807170, and 41701442), and Public Welfare Scientific Institutions of Fujian Province (2022R1002005). The authors appreciate the comments provided and encouragement made by the reviewers and the editor.
Data availability statements.
Data for this research are included in the article.
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