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  • View in gallery

    The annual (blue) and 10-yr-average (red) time series for (a) temperature (°C) over China and (b) the EASM index. Solid lines in (a) and (b) represent the linear regression of the annual times series, and the shaded bands indicate the 95% confidence intervals for the regression. (c) The locations of the eight regional divisions in China.

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    Spatial distributions of (a),(c),(e),(g) the mean pattern and (b),(d),(f),(h) trends in the annual precipitation amount, number of wet days, R95pTOT, and Gini coefficient across China for the period 1957–2014. Stippling indicates locations where the degree of change was statistically significant at the 95% confidence level.

  • View in gallery

    Comparison of the number of grids with trends that were significant at the 5% level, for (a)–(h) the eight subregions and (i) mainland China. Actual results (blue) and the 95th percentile of 1000 bootstrap resamples (yellow).

  • View in gallery

    Annual regional mean (blue) and 10-yr moving average (red) of the regional mean for (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) Gini coefficient across China during the period 1957–2014, calculated from 10-yr moving windows. Shaded bands indicate the 95% confidence intervals of the regression.

  • View in gallery

    Annual spatial variance (blue) and 10-yr moving average of the spatial variance (red) for (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) Gini coefficient across China during the period 1957–2014, calculated from 10-yr moving windows. Shaded bands indicate the 95% confidence intervals of the regression.

  • View in gallery

    Spatial distribution of the dominant factors underlying long-term changes in precipitation indices during the 1957–2014 period, for (a),(c),(e),(g) regions with |WT| > |WM| (color shading indicates the values of WT) and (b),(d),(f),(h) regions with |WT| < |WM| (color shading indicates the values of WM). Stippling indicates locations where the contribution was statistically significant at the 95% confidence level.

  • View in gallery

    Observed percentage changes in daily precipitation (a) frequency and (b) intensity from 1957–85 to 1986–2014 for eight subregions in China and rates of change in the (c) frequency and (d) intensity of different daily precipitation during the period 1957–2014 for the eight subregions. Long-term changes in the daily precipitation (e) frequency and (f) intensity induced by temperature (open bars) and the EASM (filled bars), for each of the eight subregions.

  • View in gallery

    Spatial distribution of the dominant factors underlying interannual variability in the precipitation indices during the 1957–2014 period, for (a),(c),(e),(g) regions with |IT| > |IM| (color shading indicates the values of IT) and (b),(d),(f),(h) regions with |IT| < |IM| (color shading indicates the values of IM). Stippling indicates locations where the contribution was statistically significant at the 95% confidence level.

  • View in gallery

    Interannual variability of the daily precipitation (a) frequency and (b) intensity induced by changes in temperature (open bars) and the EASM (filled bars), for the eight subregions.

  • View in gallery

    Composite analysis of (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) the Gini coefficient averaged over different EASM years. Plots show the difference (SlowShigh) between the values during weak EASM years (the 10 years with the lowest EASM index values) and strong EASM years (the 10 years with the highest EASM index values). The strong and weak EASM years were chosen on the basis of the original EASM index time series. Stippling indicates locations where the difference was statistically significant at the 95% confidence level.

  • View in gallery

    Integrated water vapor flux divergence anomalies related to (a) weak and (b) strong EASM years, relative to the baseline time period (1961–90), and (c) the difference in the integrated water vapor flux divergence between the weak and strong EASM years. Also shown are integrated water vapor flux anomalies related to (d) weak and (e) strong EASM years, relative to the baseline time period (1961–90), and (f) the difference in integrated water vapor flux between weak and strong EASM years. The strong and weak EASM years were chosen on the basis of the original EASM index time series.

  • View in gallery

    The differences in the anomalous wind fields and the 588-gpdam [1 geopotential dekameter (gpdam) = 10 gpm] height at 500 hPa between the weak (red shading) and strong (blue shading) EASM years in (a) June, (b) July, and (c) August, and the difference in outgoing longwave radiation (OLR) between the weak and strong EASM years in (d) June, (e) July, and (f) August. The strong and weak EASM years were chosen on the basis of the original EASM index time series.

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Changes in the Spatial Heterogeneity and Annual Distribution of Observed Precipitation across China

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  • 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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Abstract

This study focuses on changing trends in the spatial variance and annual distribution of precipitation across mainland China during the period 1957–2014. The influence on precipitation of temperature, the East Asian summer monsoon (EASM), and related atmospheric circulation variables are examined to explore the underlying mechanisms driving the changes in precipitation. Statistically significant downward trends in the number of wet days were observed in humid regions. Large parts of southeastern China featured high temporal inequality of rainfall over the course of a year, with extreme precipitation events contributing a relatively large percentage of the total annual precipitation. Arid regions generally showed statistically significant upward trends in the number of wet days and in the fraction of extreme precipitation but a decrease in the temporal inequality. These spatial heterogeneities indicate that extreme precipitation became more widespread across mainland China. Temperature dominated the long-term changes in precipitation indices over large regions of mainland China, except in the Jianghuai region, where the weakening EASM induced greater precipitation and a more uneven annual distribution of precipitation. The effects of temperature on precipitation were region dependent and varied with precipitation intensity. This contributed to the overall decrease in the spatial variance of extreme precipitation and the increase in the temporal inequality of precipitation over eastern China. However, the EASM was more important for the interannual variability of precipitation indices over the west of northwestern China, the Yanghuai region, and some grids in southern China. The EASM exerted a zonal influence on precipitation variability through the modulation of water vapor patterns, wind fields, and convection activities.

Denotes content that is immediately available upon publication as open access.

© 2017 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: Chiyuan Miao, miaocy@vip.sina.com

Abstract

This study focuses on changing trends in the spatial variance and annual distribution of precipitation across mainland China during the period 1957–2014. The influence on precipitation of temperature, the East Asian summer monsoon (EASM), and related atmospheric circulation variables are examined to explore the underlying mechanisms driving the changes in precipitation. Statistically significant downward trends in the number of wet days were observed in humid regions. Large parts of southeastern China featured high temporal inequality of rainfall over the course of a year, with extreme precipitation events contributing a relatively large percentage of the total annual precipitation. Arid regions generally showed statistically significant upward trends in the number of wet days and in the fraction of extreme precipitation but a decrease in the temporal inequality. These spatial heterogeneities indicate that extreme precipitation became more widespread across mainland China. Temperature dominated the long-term changes in precipitation indices over large regions of mainland China, except in the Jianghuai region, where the weakening EASM induced greater precipitation and a more uneven annual distribution of precipitation. The effects of temperature on precipitation were region dependent and varied with precipitation intensity. This contributed to the overall decrease in the spatial variance of extreme precipitation and the increase in the temporal inequality of precipitation over eastern China. However, the EASM was more important for the interannual variability of precipitation indices over the west of northwestern China, the Yanghuai region, and some grids in southern China. The EASM exerted a zonal influence on precipitation variability through the modulation of water vapor patterns, wind fields, and convection activities.

Denotes content that is immediately available upon publication as open access.

© 2017 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: Chiyuan Miao, miaocy@vip.sina.com

1. Introduction

Global warming is believed to lead to changes in the characteristics of precipitation (PR) and may intensify the hydrological cycle. A suite of studies has revealed the spatiotemporal changes in precipitation resulting from global warming and the related impact on human society and the natural environment (Dai et al. 2007; Ma et al. 2015; Trenberth 2011; Trenberth et al. 2003). More recent studies have found complex and spatially heterogeneous patterns of precipitation change (Donat et al. 2013; Ghosh et al. 2011; Rajah et al. 2014). In terms of spatial distribution, future precipitation is predicted to increase at high latitudes and in the vicinity of the equator, but to decrease in the subtropics (Watterson and Whetton 2011). The midlatitudes are expected to experience increases in the number of consecutive dry days, but northerly latitudes and central Africa are projected to show decreases in the number of consecutive dry days (IPCC 2012). The amplitude of precipitation changes between dry and wet regions is subject to uncertainty and controversy. Values for extreme daily precipitation averaged over both dry and wet regimes show robust increases over the past six decades, demonstrated both by observations and by climate models (Donat et al. 2016). In terms of temporal variation, Rajah et al. (2014) found a downward trend in the number of wet (PR > 0.1 mm day−1) days in East Asia, Central America, and Brazil over the period 1976–2000, whereas western Europe and Australia experienced an increase in the number of wet and light precipitation days over the same period.

The climate in China varies greatly over both space and time owing to the country’s complex topography and large zonal and meridional range (Miao et al. 2016b; Xu et al. 2015). A number of studies have examined changes in the spatiotemporal characteristics of precipitation (Lu et al. 2014; Wu et al. 2016; Zhai et al. 2005). The frequency of light (0.1 ≤ PR < 10 mm day−1) and moderate (10 ≤ PR < 25 mm day−1) precipitation events across China show significant downward trends (Qian et al. 2007), whereas the frequency of very heavy events (PR ≥ 50 mm day−1) has increased significantly (Ma et al. 2015). There are no clear trends in total precipitation for China as a whole, but there are distinctive regional and seasonal patterns (Zhai et al. 2005). Southeastern and northeastern China are dominated by a wetting tendency, with an increase in precipitation intensity, whereas southwestern China exhibits a significant drying tendency (Xiao et al. 2016). Since the late 1990s, there have been different decadal variation features in summer precipitation from north to south in China: a reduction in summer precipitation in northeast and north China and from the lower to middle reaches of the Yangtze River after 1999, but a remarkable decrease in summer precipitation over southern and southwestern China after 2003 (Xu et al. 2015). Liu et al. (2015) indicates that the changing trends in precipitation extremes in southwestern China are not spatially uniform and that the spatial variance of precipitation extremes decreased between 1959 and 2012.

Preexisting research has generally focused on detecting trends in mean precipitation and precipitation extremes, or on changes in different categories of precipitation. Few studies have quantitatively assessed the spatial variance and annual distribution of precipitation in China. This study differs from previous analyses in that we examine changes in the spatial variance and annual temporal inequality of precipitation instead of changes in the basic characteristics of precipitation. Knowledge of changes in the spatial variance and annual distribution of precipitation allows for a more comprehensive view of spatial and temporal imbalances in water availability. This in turn has significant implications for related policy and engineering projects, such as the South-to-North Water Diversion Project. Moreover, we also investigate the mechanisms that contribute to the spatial variance and annual distribution of precipitation. This is essential for disaster evaluation because of the influence of these aspects of precipitation on the spatiotemporal distribution of floods and droughts (Ghosh et al. 2011; Liu et al. 2015). Changes in precipitation can be triggered by many complex factors, including natural variability and anthropogenic changes in external forcings. Changes in temperature can intensify the hydrological cycle, exerting a significant influence on mean and extreme regional precipitation (Fischer and Knutti 2015), with Clausius–Clapeyron (C-C) and “super C-C” relationships reported previously (Ivancic and Shaw 2016; Lenderink and Van Meijgaard 2008; Miao et al. 2016b). China is located in the region of the East Asian monsoon; thus the variability of precipitation over China, especially extreme precipitation, is believed to be directly related to the East Asian summer monsoon (EASM). The EASM affects the occurrence of extensive drought and flood disasters in East Asia by controlling shifts in the major seasonal rain belt and producing unusual patterns (Ding 1992; Wang and LinHo 2002; Zhang et al. 2017). Several studies have shown that the EASM has weakened since the end of the 1970s (Wang 2001; Yu et al. 2004). The severe and persistent droughts that have occurred in recent decades can be partly attributed to the weakening of the EASM, leading to weaker northward moisture transport and a deficient moisture supply for northern China (Ding et al. 2008). ENSO and the PDO are climate signals from the oceans and can trigger pronounced climate change across the world (Dai and Wigley 2000; Diaz et al. 2001; Dong and Dai 2015; Barlow 2001; Sun et al. 2016; Miao et al. 2016a; Zhang et al. 2013). ENSO has been shown to have an extremely strong and robust influence on seasonal rainfall in East Asia, which has been mainly ascribed to the interactions between ENSO and the East Asian summer and winter monsoons (Karori et al. 2013; Ying et al. 2015; Zhou and Wu 2010). The PDO plays a role in the dry/wet conditions in northern China by modulating the EASM (Qian and Zhou 2014; Yang et al. 2005). A “southern flood/northern drought” pattern has been observed in China during positive PDO phases, with the inverse pattern observed during negative PDO phases, demonstrating the influence of PDO-related ocean–atmosphere interactions on the EASM during different PDO phases (Fu et al. 2009; Ouyang et al. 2014; Yang et al. 2005).

The objectives of this study are 1) to investigate long-term changes in the spatial heterogeneity and annual distribution of precipitation in China during the period 1957–2014 and 2) to address the linkage of these changes to changes in temperature and in the EASM.

2. Data and methods

a. Data

Observed daily precipitation data for the period 1957–2014 were obtained from the National Meteorological Information Center of the China Meteorological Administration. This dataset is constructed from over 2400 station observations across China at a resolution of 0.5° × 0.5° (Shen et al. 2010). Data on local changes in temperature were extracted from the Global Historical Climatology Network (GHCN)–Climate Anomaly Monitoring System (CAMS) gridded 2-m temperature dataset, which is a high resolution (0.5° × 0.5°) dataset of global land surface temperatures from 1948 to the near present (Fan and Van den Dool 2008). The EASM index is defined as the area-averaged seasonally (June–August) dynamical normalized seasonality at 850 hPa within the East Asian monsoon domain (10°–40°N, 110°–140°E) (Li and Zeng 2002, 2003) and indicates weak and strong EASM years. Wind fields, geopotential height fields, specific humidity, and surface pressure were obtained from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NCEP-1; Kalnay et al. 1996). The global NCEP-1 provides data at a horizontal resolution of 2.5° × 2.5° at different pressure levels. Figure 1 shows the time series for temperature over China and the EASM index. To investigate atmospheric water vapor transport, we calculated the vertically integrated water vapor flux from the surface to 300 hPa and the corresponding water vapor flux divergence during the summer from the data for wind fields, specific humidity, and surface pressure. We calculated the regional mean summer water vapor flux (MWVF), the regional mean water vapor flux divergence (MWVFD), the spatial variability of water vapor flux (SWVF), and the spatial variability of water vapor flux divergence (SWVFD) to assess their relationship with precipitation variability. The NOAA interpolated outgoing longwave radiation (OLR) dataset (Liebmann and Smith 1996) was used to measure the amount of energy emitted to space by Earth’s surface, oceans, and atmosphere; OLR values are often used as a proxy for convection in tropical and subtropical regions because cloud-top temperatures are an indicator of cloud height (colder is higher) (Liebmann and Smith 1996).

Fig. 1.
Fig. 1.

The annual (blue) and 10-yr-average (red) time series for (a) temperature (°C) over China and (b) the EASM index. Solid lines in (a) and (b) represent the linear regression of the annual times series, and the shaded bands indicate the 95% confidence intervals for the regression. (c) The locations of the eight regional divisions in China.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

b. Analysis methods

Three precipitation indices—the amount of precipitation, the frequency of wet days, and the fraction of precipitation due to very wet days (R95pTOT)—were used to investigate variations in precipitation. Wet days were defined as days with more than 1.0 mm precipitation. We classified wet days into three types: normal wet days, moderate wet days, and very wet days based on the 75th and 95th percentiles of wet-day precipitation during the period 1961–90. Very wet days were defined as days with more precipitation than the 95th percentile of 1961–90 wet-day precipitation. Moderate wet days were defined as days with more precipitation than the 75th percentile but less precipitation than the 95th percentile of 1961–90 wet-day precipitation. Normal wet days were defined as days with precipitation equal to or less than the 75th percentile of 1961–90 wet-day precipitation. R95pTOT indicates the percentage of total precipitation that occurred on very wet days (Karl et al. 1999). In addition, the Gini coefficient was calculated to assess the annual distribution of rainfall and to quantify the level of inequality in the temporal distribution of precipitation over the course of year. The Gini coefficient was calculated according to the method described by Rajah et al. (2014). At each grid location, daily precipitation was sorted in increasing order and expressed as the series Pi (i = 1, … , n; n = 365 days in normal years, and n = 366 days in leap years). Then the order series was summed cumulatively and converted to percentages of the total precipitation for that year, thus constructing a Lorenz curve. The Gini coefficient G for precipitation is defined as double the area between the line of perfect equality (y = x) and the Lorenz curve, and can be calculated from the equation described by Rajah et al. (2014):
e1
The Gini coefficient ranges from 0 to 1 and indicates the degree of uniformity in the annual precipitation distribution: G = 0 indicates that precipitation was uniformly distributed throughout the year, whereas G = 1 indicates that the entire annual precipitation occurred on a single day (Rajah et al. 2014; Sun et al. 2015). A downward (upward) trend in the Gini coefficient indicates that the distribution of annual precipitation within each year has become more uniform (nonuniform).
The nonparametric Mann–Kendall test was used to determine the statistical significance of the trends in precipitation indices at each grid point (Mann 1945) and trend magnitudes were estimated with Sen’s slope estimator (Sen 1968). A 5% significance level was used in all significance tests. We implemented a technique suggested by Livezey and Chen (1983) to determine whether our results were field significant. The annual time series for each grid was randomly shuffled by year, with all grids shuffled the same way to retain the spatial correlations. This involved creating 1000 bootstrapped fields using a bootstrap resampling technique. For each of 1000 random trials, we calculated the number of grids with a local trend significant at the 5% level. These 1000 values were then used to calculate the 95th percentile of the number of grids with significant trends; if the actual number of grids with significant trends in the unshuffled data exceeds the bootstrapped 95th percentile, then the trend can be said to be field significant. We used the spatial variance υ calculated across all grid locations to represent the spatial variability of each precipitation index in each year, as follows:
e2
where N is the number of grids in mainland China, A is the annual precipitation index value at each grid, and μ is the regional mean for that precipitation index.
We investigated the influence of local temperature changes, the EASM, and related atmospheric circulation variables on changes in the spatial variance and annual distribution (G) of precipitation over China. We used the method described in Lu et al. (2010, 2014) to quantify the contributions of temperature and the EASM to the long-term trends and interannual variability in the precipitation indices. We assumed that the precipitation index P is a function of only temperature T and the EASM M, that is, P = P(T, M). We conducted a multiple regression of P against both T and M, and ∂P/∂T and ∂P/∂M were calculated as coefficients of the multiple regression. To measure the contributions of temperature and the EASM for long-term trends, the rates of change and the absolute values for the changes in temperature ΔT in each grid and the EASM index ΔM were used to calculate WT and WM:
e3
e4
For interannual variability, IT and IM were used to estimate the variability in the precipitation indices induced by variations in T and M:
e5
e6
where σT and σM are the standard deviations of temperature in each grid and the EASM index.

We also conducted a composite analysis to estimate the influence of the EASM. This method provides a direct view of the possible influence of modes of large-scale variability on the spatial and temporal variations in precipitation (Zhang et al. 2010). From the period 1957–2014, we first selected the years with the 10 highest (1972, 1961, 1960, 2012, 1985, 1963, 1997, 1967, 2002, and 1984) and 10 lowest (1998, 1980, 1988, 2010, 1996, 1983, 2008, 1995, 2013, and 2003) EASM index values. We then computed the mean value for each precipitation index for the weak monsoon years and the strong monsoon years (Slow and Shigh, respectively). We calculated the difference in those means (SlowShigh) as a measure of the influence of ocean signals and used two-sided Student’s t tests to determine whether the composite differences were statistically significant. For the analyses, China was divided into eight subregions on the basis of administrative divisions and the characteristics of the Chinese monsoon climate (Fig. 1c; Shi and Xu 2007).

3. Results

a. Spatial distributions and trends in the mean precipitation indices

Figure 2 shows the spatial distributions of the mean and the mean annual change for four precipitation indices (precipitation amount, number of wet days, R95pTOT, and the Gini coefficient) calculated for the period 1957–2014. Precipitation amount and the number of wet days decreased gradually from southeastern to northwestern China, consistent with known precipitation patterns. The trends in precipitation amount and number of wet days were spatially nonuniform (Figs. 2b,d). Statistically significant downward trends in the number of wet days were found in southwest China and in various grids scattered on the North China Plain. In contrast, large areas of western China showed statistically significant upward trends in both precipitation amount and number of wet days. The annual change in the number of wet days ranges from −0.5 to 0.5 days yr−2, with the strongest downward trends located in southwest China and the strongest upward trends located on the southern Tibetan Plateau. The values for R95pTOT were larger for eastern China than western China. The humid regions are generally characterized by relatively more extreme events, resulting in a relatively larger contribution from extreme events to the total amount of precipitation. The arid regions, however, have fewer wet days and not enough moisture to form extreme precipitation; the amount of precipitation from an extreme rainfall event therefore occupies a small proportion of the total annual precipitation. The Gini coefficient quantifies inequalities in the annual distribution of daily precipitation. The spatial distribution of the mean Gini coefficient was opposite to that for the number of wet days (Fig. 2c). In southeastern China, statistically significant upward trends were found for both R95pTOT and the Gini coefficient. However, opposite trends in R95pTOT and the Gini coefficient were observed in northwestern China and in some scattered grid locations in northeast China, with upward trends in R95pTOT and downward trends in the Gini coefficient. The changes in the Gini coefficient could largely be attributed to changes in the number of wet days, which had an almost mirror-image spatial distribution (Fig. 2d). Except for precipitation amount, the changes were field significant for all precipitation indices at the national scale. At the subregional scale, all of the changes were field significant for all of the precipitation indices for southwest China, the Tibetan Plateau, and the west of northwest China (Fig. 3).

Fig. 2.
Fig. 2.

Spatial distributions of (a),(c),(e),(g) the mean pattern and (b),(d),(f),(h) trends in the annual precipitation amount, number of wet days, R95pTOT, and Gini coefficient across China for the period 1957–2014. Stippling indicates locations where the degree of change was statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

Fig. 3.
Fig. 3.

Comparison of the number of grids with trends that were significant at the 5% level, for (a)–(h) the eight subregions and (i) mainland China. Actual results (blue) and the 95th percentile of 1000 bootstrap resamples (yellow).

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

b. Trends in the spatial variance of the precipitation indices

In addition to the nonuniform spatial changes in precipitation over China described above, the regional mean and the variance of precipitation (calculated across all grid locations in each year) showed changes over time. At the annual scale, there were no significant trends in the precipitation indices (Fig. 4). However, at the decadal scale, significant trends were observed in the regional mean and the spatial variance for almost all indices (Figs. 4 and 5). For precipitation amount, the regional mean and spatial variance showed significant upward trends over the period 1957–2014. A periodic characteristic was observed in the spatial variance of the precipitation amount: there was a marked increase in spatial variance during the 1990s but a decrease in spatial variance after the year 2000. The trends in the regional mean and spatial variance for the number of wet days were opposite to each other. The number of wet days tended to increase but became increasingly spatially homogeneous across China; there was an upward trend in the regional mean but a downward trend in spatial variance. This pattern was in large part due to a significant increase in the number of wet days over northwestern China. Extremes in climate can easily trigger tremendous damage to both social and natural systems. Figure 4c shows that the R95pTOT regional mean across China increased over the period 1957–2014 whereas the spatial variance of R95pTOT decreased over the same period. The spatial variance of R95pTOT exhibited an overall rate of change of −0.60% yr−1. The nonuniform spatial trend in the Gini coefficient resulted in a significant downward trend in spatial variance over time (Fig. 5d).

Fig. 4.
Fig. 4.

Annual regional mean (blue) and 10-yr moving average (red) of the regional mean for (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) Gini coefficient across China during the period 1957–2014, calculated from 10-yr moving windows. Shaded bands indicate the 95% confidence intervals of the regression.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

Fig. 5.
Fig. 5.

Annual spatial variance (blue) and 10-yr moving average of the spatial variance (red) for (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) Gini coefficient across China during the period 1957–2014, calculated from 10-yr moving windows. Shaded bands indicate the 95% confidence intervals of the regression.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

c. Factors affecting the spatial variance and annual distribution of precipitation

The annual and decadal regional means for precipitation amount and R95pTOT were positively correlated with temperature anomalies at the 95% confidence level (Tables 13), indicating that China is expected to experience an increased frequency of wet days and extreme precipitation with climate warming. The relationships were more significant at the decadal scale than the annual scale. The correlation coefficients for the detrended annual series were not significant, except for the strong relationship between R95pTOT and the EASM (Table 2). With the exception of precipitation amount, decadal spatial variance was negatively correlated with temperature for the number of wet days and the Gini coefficient. At the annual scale, the EASM had significant negative relationships with the regional means for precipitation amount and R95pTOT but was not significantly correlated with the spatial variance of the precipitation indices. Furthermore, in addition to the significant trends in the smoothed series, there were also some decadal-scale changes (Figs. 4 and 5). As suggested by significant correlation coefficients reported in Table 3, the EASM generally plays a dominant role in the decadal-scale changes in the regional precipitation index means. The decadal regional means for precipitation amount, the number of wet days, and R95pTOT were significantly correlated with the EASM index, which also had a positive relationship with the Gini coefficient. There was a positive correlation between the EASM and the decadal spatial variance of R95pTOT but a negative correlation with precipitation amount. Atmospheric water flux and divergence contribute crucially to the formation of precipitation events, especially extreme precipitation. Regional means at the decadal scale were closely related to the mean water vapor flux for all precipitation indices, with high correlation coefficients at the 95% confidence level (Table 3). The decadal spatial variances of precipitation amount and number of wet days were correlated with the mean and spatial variance of the water vapor flux divergence.

Table 1.

Correlation coefficients for the relationships between the EASM, temperature, atmospheric water vapor flux, and the regional mean and spatial variance of the precipitation indices, assessed at the annual scale (an asterisk denotes p < 0.05).

Table 1.
Table 2.

Correlation coefficients for the relationships between the detrended time series for the EASM, temperature, atmospheric water vapor flux, and the regional mean and spatial variance of the precipitation indices, assessed at the annual scale (an asterisk denotes p < 0.05).

Table 2.
Table 3.

Correlation coefficients for the relationships between the EASM, temperature, atmospheric water vapor flux, and the regional mean and spatial variance of the precipitation indices, assessed at the decadal scale on the basis of a 10-yr-average time series (an asterisk denotes p < 0.05).

Table 3.

1) Linking the long-term trends in precipitation to temperature and the East Asian summer monsoon

Both temperature and the EASM are important factors affecting precipitation variability over China. We used WT and WM to measure the long-term changes in precipitation induced by changes in temperature and the EASM index (Fig. 6). Temperature changes were the dominant factor affecting long-term changes in the precipitation indices for large regions of mainland China. As temperatures warmed, the precipitation amount and number of wet days decreased and the annual distribution of precipitation became more uneven in humid and subhumid regions concentrated in eastern China. Conversely, the arid regions, especially the Tibetan Plateau, became wetter, with increases in the precipitation amount and number of wet days (Figs. 6a,b). In some grids located in northwestern China, the upward trends observed for precipitation amount and number of wet days may be a response to weakening of the EASM to some extent, though the correlation coefficients were not significant. In some grids in the Jianghuai region, the EASM was relatively more important for changes in R95pTOT and the annual temporal distribution (Figs. 6c,d).

Fig. 6.
Fig. 6.

Spatial distribution of the dominant factors underlying long-term changes in precipitation indices during the 1957–2014 period, for (a),(c),(e),(g) regions with |WT| > |WM| (color shading indicates the values of WT) and (b),(d),(f),(h) regions with |WT| < |WM| (color shading indicates the values of WM). Stippling indicates locations where the contribution was statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

To obtain a deeper understanding of the relationship between temperature, the EASM, and the spatiotemporally uneven precipitation index distributions, we divided China into eight subregions and estimated the contributions of the EASM and temperature to the long-term trends in precipitation for each subregion. Previous studies have revealed that the increase in atmospheric water-holding capacity associated with a temperature increase, described by the C-C relation, considerably influences extreme precipitation intensity; and the effects of temperature vary with different intensities of extreme precipitation. Therefore, we classified daily precipitation as normal, moderate, or very wet according to different percentiles. Then, for each category, we calculated the percentage change in frequency during the period 1986–2014 relative to the period 1957–85 and the change trends for the full period 1957–2014 for each of the eight subregions. We also estimated the changes in the 75th, 95th, and 99th percentiles of daily precipitation to quantify changes in intensity (Fig. 7). Changes in frequency differed with intensity and region. The number of normal wet days decreased in regions 2, 3, 4, 5, and 6 but increased in regions 1, 7, and 8 (Figs. 7a,c). Except for region 2, all regions showed an increase in the frequency of very wet days and a positive trend in the number of very wet days. The more intense the heavy rainfall event, the greater the relative increase in frequency. Furthermore, the 95th percentile of daily precipitation increased in all regions except 1, 2, and 7. The consistent trends in frequency and intensity for different precipitation events contributed to the changes in R95pTOT and the Gini coefficient. The region-dependent rates of change were consistent with the changes in the spatial variance of the precipitation indices.

Fig. 7.
Fig. 7.

Observed percentage changes in daily precipitation (a) frequency and (b) intensity from 1957–85 to 1986–2014 for eight subregions in China and rates of change in the (c) frequency and (d) intensity of different daily precipitation during the period 1957–2014 for the eight subregions. Long-term changes in the daily precipitation (e) frequency and (f) intensity induced by temperature (open bars) and the EASM (filled bars), for each of the eight subregions.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

We used multiple regression to quantify the contributions of temperature and the EASM to long-term changes in precipitation. Variations in temperature contributed relatively more to the changes in the frequency of wet events in most regions except region 3, in which the EASM was the dominant factor for the number of very wet days and precipitation intensity (Figs. 7e,f). The precipitation index and/or temperature ratios varied by region, indicating that the effects of temperature on precipitation are region dependent (Fig. 7e). In semihumid regions (regions 2 and 3), the relationship between the number of wet days and temperature was generally negative and the sensitivity of normal wet days to temperature was higher than the sensitivity of very wet days to temperature. In humid regions (regions 4 and 5), temperature was positively correlated with the frequency of very wet days but negatively correlated with the frequency of normal wet days, thus resulting in the upward trends in R95pTOT and the Gini coefficient with temperature seen in Fig. 2. Accordingly, the annual distribution of precipitation in humid regions became increasingly uneven with precipitation concentrated on a smaller number of days in the year, increasing the likelihood of flooding and droughts. In the arid region of northwestern China, increased temperatures may intensify the hydrological cycle and increase the number of wet days. The more intense the heavy rainfall event is, the higher the sensitivity to temperature.

2) Linking the interannual variability in precipitation to temperature and the East Asian summer monsoon

We used IT and IM to measure the relative importance of temperature and the EASM to interannual variability in the precipitation indices (Fig. 8). Temperature played a relatively more important role in the interannual variability of precipitation amount and number of wet days in north China (Figs. 8a,c; negative coefficients). In northwestern China and some grid locations scattered in southeastern and northeastern China, year-to-year variations in the EASM were more important than variations in temperature for the interannual variability of precipitation amount, number of wet days, and the Gini coefficient (Figs. 8a,c,g). In some grids of eastern China, the EASM played a more important role in the interannual variability of R95pTOT (Fig. 8f). In particular, the R95pTOT in the Yanghuai region showed a significant relationship with the EASM, indicating a strong linkage between the EASM and extreme precipitation.

Fig. 8.
Fig. 8.

Spatial distribution of the dominant factors underlying interannual variability in the precipitation indices during the 1957–2014 period, for (a),(c),(e),(g) regions with |IT| > |IM| (color shading indicates the values of IT) and (b),(d),(f),(h) regions with |IT| < |IM| (color shading indicates the values of IM). Stippling indicates locations where the contribution was statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

For each of the eight subregions, we measured the contributions of temperature and the EASM to the frequency of different precipitation events and the values of different precipitation percentiles. Figure 9 shows that the EASM had a greater influence than temperature on the interannual variability of the different precipitation frequencies in regions 1, 3, 7, and 8. In weak EASM years, the frequency of wet days increased in regions 1, 7, and 8, and the absolute percentage change increased with intensity. Region 3 experienced more very wet days but fewer normal wet days, and a higher value for the 95th percentile of precipitation. This contributed to a higher fraction of precipitation occurring as extreme precipitation and a more uneven annual distribution of precipitation in weak EASM years.

Fig. 9.
Fig. 9.

Interannual variability of the daily precipitation (a) frequency and (b) intensity induced by changes in temperature (open bars) and the EASM (filled bars), for the eight subregions.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

We applied a method known as composite analysis to assess the influence of the EASM on changes in precipitation characteristics (Fig. 10). This method is commonly used to examine the influence of large-scale circulation factors on climate variability. Increases in annual precipitation and the number of wet days were detected during weak EASM years in large parts of northwestern China (Figs. 10a and 10b, respectively). The amount of precipitation and the number of wet days decreased during weak EASM years across southeastern China and the North China Plain. For some grids located in south and northwest China, the values for R95pTOT tended to be higher during the weak EASM years than during the strong EASM years. Conversely, the North China Plain had lower R95pTOT values during the weak EASM years (Fig. 10c). These different zonal patterns related to the EASM were conducive to decreased spatial variance in R95pTOT in weak EASM years but increased spatial variance in strong EASM years. The annual distribution of precipitation was more uniform during the weak EASM years over the North China Plain, northwestern China, and the Tibetan Plateau, as demonstrated by lower Gini coefficient values in these regions (Fig. 10d). This resulted in an overall downward trend in the regional mean for the Gini coefficient. The annual distribution of precipitation was more uneven during the weak EASM years in southeastern China.

Fig. 10.
Fig. 10.

Composite analysis of (a) precipitation amount, (b) number of wet days, (c) R95pTOT, and (d) the Gini coefficient averaged over different EASM years. Plots show the difference (SlowShigh) between the values during weak EASM years (the 10 years with the lowest EASM index values) and strong EASM years (the 10 years with the highest EASM index values). The strong and weak EASM years were chosen on the basis of the original EASM index time series. Stippling indicates locations where the difference was statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

We also investigated large-scale circulation and water vapor patterns during different EASM phases (Fig. 11). During the weak EASM years, water vapor flux decreased over eastern China (Fig. 11d), which may have induced the observed decrease in the number of wet days (Fig. 10b). The pattern of the water vapor flux divergence anomaly was similar to the composite pattern for R95pTOT. The regions with a negative anomaly in the water vapor flux divergence formed a moisture sink, enabling the formation of extreme precipitation and an increase in R95pTOT. Moreover, during weak EASM years in south China, a decrease in the number of wet days (owing to a decrease in water vapor flux) simultaneous with an increase in R95pTOT (owing to a decrease in water vapor flux divergence) induced a more uneven distribution of precipitation throughout the year (Fig. 10d). The EASM is substantially regulated by the western Pacific subtropical high (WPSH), the thermal contrast between the Pacific Ocean and the Eurasian continent, and the large-scale Eurasia–Pacific atmospheric circulation (Wang and Chen 2012). In weak EASM years, the WPSH generally shifts southward and has a westward extension from June to August (Fig. 12). Northerly and southerly wind anomalies appeared in south and north China, respectively, resulting in the convergence of cold air from the north with warm air from the south and an increase in extreme precipitation events in region 3. Moreover, in weak EASM years, large parts of eastern and northwestern China may become active regions for convection owing to the negative OLR anomaly in the summer. Consequently, one might expect an increase in total precipitation amount and extreme precipitation in south China because of the abundant moisture and convective activities present in large parts of southeastern China during weak EASM years, especially in the Yangtze River basin. In contrast, precipitation amount and the number of wet days tended to decrease over large areas of north China during weak EASM years because of the concurrence of an increase in water vapor flux divergence and a decrease in water vapor. Hence, the EASM resulted in different zonal tendencies and thereby increased the spatial variance of precipitation characteristics.

Fig. 11.
Fig. 11.

Integrated water vapor flux divergence anomalies related to (a) weak and (b) strong EASM years, relative to the baseline time period (1961–90), and (c) the difference in the integrated water vapor flux divergence between the weak and strong EASM years. Also shown are integrated water vapor flux anomalies related to (d) weak and (e) strong EASM years, relative to the baseline time period (1961–90), and (f) the difference in integrated water vapor flux between weak and strong EASM years. The strong and weak EASM years were chosen on the basis of the original EASM index time series.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

Fig. 12.
Fig. 12.

The differences in the anomalous wind fields and the 588-gpdam [1 geopotential dekameter (gpdam) = 10 gpm] height at 500 hPa between the weak (red shading) and strong (blue shading) EASM years in (a) June, (b) July, and (c) August, and the difference in outgoing longwave radiation (OLR) between the weak and strong EASM years in (d) June, (e) July, and (f) August. The strong and weak EASM years were chosen on the basis of the original EASM index time series.

Citation: Journal of Climate 30, 23; 10.1175/JCLI-D-17-0045.1

4. Discussion and conclusions

We presented here an analysis of the changing spatial and temporal variability in precipitation across mainland China during the period 1957–2014. To investigate the underlying mechanisms driving these changes, we examined the influence of temperature, the East Asian summer monsoon, and related atmospheric circulation variables. Our results show that changes in annual precipitation and the number of wet days were not spatially uniform across China but instead have strong regional variations. Statistically significant downward trends in the number of wet days were observed in humid regions. In general, large regions of southeastern China featured an increased contribution to the overall precipitation amount from extreme events and higher temporal inequality of rainfall over the course of a year, as demonstrated by statistically significant upward trends in R95pTOT and the Gini coefficient. However, arid regions generally showed statistically significant upward trends in precipitation amount, the number of wet days, and the fraction of annual precipitation from extreme events, but a decrease in the temporal inequality of rainfall (Fig. 2). These spatially nonuniform trends contributed to changes in the spatial variance of precipitation.

A number of studies have suggested that a warmer atmosphere tends to hold more moisture and, within the context of climate warming, induces extreme rainfall events with high rainfall intensity (Lenderink and van Meijgaard 2008; Pall et al. 2007). Our results reveal that regional mean precipitation indices were positively correlated with temperature. Long-term changes in the precipitation indices were dominated by temperature changes over large regions of mainland China. The effects of temperature on precipitation were region dependent. In humid and subhumid regions concentrated in eastern China, precipitation amount and the number of wet days decreased and the annual distribution of precipitation became more uneven; conversely, arid regions became wetter, with increases in the precipitation amount and the number of wet days. In the context of climate warming, more extreme precipitation occurred widely across mainland China, resulting in an increase in mean R95pTOT and a decrease in the spatial variance of R95pTOT across China. This may be related to the fact that the relationship between precipitation and temperature varies with the intensity of precipitation events (Fischer and Knutti 2015; Miao et al. 2016b; Min et al. 2011). In arid regions, the more intense the heavy rainfall event is, the higher the relative increase in frequency, resulting in an increase in R95pTOT. In humid regions, there was an increase in the number of very wet days but a decrease in the number of normal wet days (Ma et al. 2015), resulting in greater unevenness in the annual distribution of precipitation and an increase in R95pTOT. Increased unevenness in the annual distribution of precipitation under a warmer climate places stress on water availability.

Temperature and the EASM are major factors that control the interannual variability of precipitation. Our results suggest that the EASM is more important than temperature for the interannual variability of precipitation indices in northeast China, north China, Jianghuai, and northwest China through its effects on the frequency of wet days (of different intensities) and on the percentiles of daily precipitation. The EASM effects had zonal characteristics with the mean and extremes of precipitation exhibiting spatially opposite patterns in weak and strong EASM years. A reduction in water vapor flux in weak EASM years resulted in fewer wet days in large parts of eastern China. In weak EASM years, a southward shift and westward extension of the WPSH, negative anomalies in the water vapor flux divergence, and active convection activities were simultaneously conducive to the formation of extreme precipitation, resulting in higher values of R95pTOT and a more uneven annual distribution of precipitation in the Jianghuai region and some parts of south China. These spatially inconsistent changes also inevitably contributed to the overall changes in spatial variance and the annual distribution of precipitation. The observed changes in the spatial variance of precipitation will likely lead to imbalances in water availability and an uneven annual distribution of precipitation could lead to temporal imbalances in water resources over the course of a year. Spatial and temporal regulation of water resources based on knowledge of the spatial and temporal variability is conducive to optimal agricultural production. Furthermore, the Jianghuai region may become more susceptible to extreme precipitation owing to the concurrent increases in both R95pTOT and precipitation amount during weak EASM years. Southern China may suffer from the concurrence of lasting persistent drought and extreme precipitation within a single year because of the uneven annual distribution of precipitation. Some regions in northern China showed a slightly drying pattern. An inverse hazard map would be predicted for strong EASM years. Therefore, it is necessary to take strict precautions against the possibility of multiple natural disasters in China, given the alternation of hazards with different climatic backgrounds (Yang et al. 2017).

The upward trends in precipitation amount and number of wet days observed in some grids located in northwestern China may to some extent be a response to the weakening of the EASM. In some grids in the Jianghuai region, the EASM was more important than the temperature for changes in R95pTOT and the annual temporal distribution of precipitation. The EASM has been dominated over the last 50 years by considerable interannual-to-decadal fluctuations, with the interdecadal variation thought to be due to the interdecadal ENSO phenomenon in the eastern tropical Pacific since the mid-1970s (Huang 2001). The EASM circulation has weakened dramatically since the late 1970s. Previous studies have suggested that anthropogenic aerosols can reduce total surface radiative forcing, cooling continental East Asia and decreasing the land–sea thermal contrast, and hence playing a nonnegligible role in weakening the EASM (Jiang and Tian 2013; Li et al. 2016). A comparison of separate forcing runs also showed a primary role of aerosol forcing in weakening the EASM in the all-forcings run (Song et al. 2014). Warming associated with tropical interdecadal variability centered over the central and eastern Pacific may have also contributed to the weakening of the EASM (Li et al. 2010). Warming in the far western Pacific Ocean induced a westward extension of the western Pacific subtropical high (Zhou et al. 2009). Moreover, meridional asymmetric warming with prominent surface warming in the mid-to-high latitudes induced a weakened meridional thermal contrast over eastern Asia, decelerated the westerly jet stream, and weakened the EASM (An et al. 2015). However, how the EASM will respond to future global warming remains an open question. Li et al. (2010) suggested that the observational and theoretical evidence, together with the results from numerical experiments, does not support the notion that the EASM tends to get stronger or weaker under surface-warming forcing. In their projections, Li et al. (2010) found that EASM strength does not show any pronounced trends in response to future global warming, but instead continues to show interannual-to-decadal variations during the twenty-first century. Jiang and Tian (2013) showed that the summer monsoon strengthens slightly over the twenty-first century owing to an increased land–sea thermal contrast between the East Asian continent and the adjacent western North Pacific and South China Sea. Additional studies based on climate model are required to fully assess the future global warming–related changes in the EASM and related precipitation variability.

In summary, temperature, the East Asian summer monsoon, and related large-scale atmospheric circulations are important factors in the spatial and temporal variability of precipitation in China, especially at the decadal scale. However, other factors, such as urbanization, land use, and topographic heterogeneity can significantly influence local microclimates and the formation of precipitation, and thereby also affect the overall spatial heterogeneity and annual distribution of precipitation (Ghosh et al. 2011; Liu et al. 2014; Liu et al. 2015). For instance, local topographic heterogeneity can affect local atmospheric water vapor transport and atmospheric convection and thus change the microclimate significantly. Liu et al. (2015) showed that the spatial variability of precipitation extremes is closely related to topographic factors. Further analysis is required to investigate the underlying mechanisms and the interactions between the different factors. The observed spatial heterogeneity and temporal inequality in precipitation indicates that there is a high risk of different natural hazards occurring in different regions throughout the year. Systematic examination of changes in the spatial variation and annual distribution of precipitation in future climate scenarios is essential for the management and mitigation of natural hazards in China.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grants 41622101, 91547118, and 41605073), the National Key Research and Development Program of China (Grant 2016YFC0501604), the Fundamental Research Funds for the Central Universities, and the State Key Laboratory of Earth Surface Processes and Resource Ecology. We are also grateful to National Meteorological Information Center (NMIC) of the China Meteorological Administration (http://data.cma.cn) for providing the observed climate data and climate signals from the oceans, as well as the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) for providing the reanalysis dataset.

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