Analysis of the Inﬂuencing Factors of Drought Events Based on GRACE Data under Different Climatic Conditions: A Case Study in Mainland China

: The occurrence of droughts has become more frequent, and their intensity has increased in mainland China. With the aim of better understanding the inﬂuence of climate background on drought events in this region, we analyzed the role of the drought-related factors and extreme climate in the formation of droughts by investigating the relationship between the drought severity index (denoted as GRACE-DSI) based on the terrestrial water storage changes (TWSCs) derived from Gravity Recovery and Climate Experiment (GRACE) time-variable gravity ﬁelds and drought-related factors/extreme climate. The results show that GRACE-DSI was consistent with the self-calibrating Palmer Drought Severity Index in mainland China, especially for the subtropical monsoon climate, with a correlation of 0.72. Precipitation (PPT) and evapotranspiration (ET) are the main factors causing drought events. However, they play different roles under different climate settings. The regions under temperate monsoon climate and subtropical monsoon climate were more impacted by PPT, while ET played a leading role in the regions under temperate continental climate and plateau mountain climate. Moreover, El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) events mainly caused abnormalities in PPT and ET by affecting the strength of monsoons (East Asian and Indian monsoon) and regional highs (Subtropical High, Siberian High, Central Asian High, etc.). As a result, the various affected regions were prone to droughts during ENSO or NAO events, which disturbed the normal operation of atmospheric circulation in different ways. The results of this study are valuable in the efforts to understand the formation mechanism of drought events in mainland China.


Introduction
Drought is a severe natural hazard event on a global scale characterized by terrestrial water deficit. It has a negative impact on socioeconomic development, crop failure, ecosystems, and the lives of people [1][2][3]. Therefore, research on the influencing factors of drought events are of great significance for establishing early warning, strengthening water resources management and reducing disaster losses [4]. The drought index has always been used as a quantitative indicator to characterize the drought events due to its simple and easy-to-understand characteristics [5]. At present, the commonly used drought indices China is a vast territory with a wide span of latitudes and many areas that are far from the sea. In addition, the terrain types and mountain directions are diverse, resulting in a diversity of temperature, PPT and climates formed. The Eastern region has a monsoon climate, the Northwest region has a temperate continental climate and the Qinghai-Tibet Plateau has a plateau mountain climate. There are also humid regions, semi-humid regions, semi-arid regions and arid regions. Furthermore, it is one of the countries with the most rivers in the world. There are more than 1500 rivers with an area larger than 1000 km 2 in MC, including the Yangtze River, Yellow River, Pearl River, Huaihe River, Liaohe River and Songhua River. Most rivers are located in Eastern and Southern China, and a few rivers are located in Northwest China. The ET gradually decreases from Southeast to Northwest China. The distribution of precipitation decreases sharply from the Southeastern (>3000 mm) to the Northwestern region (<50 mm) [30]. The occurrence of droughts and floods has become more frequent, and the intensity of drought and floods has also increased [31]. According to the climate type, MC can be divided into five parts ( Figure   1), namely temperate monsoon climate (Ⅰ), subtropical monsoon climate (Ⅱ), plateau mountain climate (Ⅲ), temperate continental climate (Ⅳ) and tropical monsoon climate China is a vast territory with a wide span of latitudes and many areas that are far from the sea. In addition, the terrain types and mountain directions are diverse, resulting in a diversity of temperature, PPT and climates formed. The Eastern region has a monsoon climate, the Northwest region has a temperate continental climate and the Qinghai-Tibet Plateau has a plateau mountain climate. There are also humid regions, semi-humid regions, semi-arid regions and arid regions. Furthermore, it is one of the countries with the most rivers in the world. There are more than 1500 rivers with an area larger than 1000 km 2 in MC, including the Yangtze River, Yellow River, Pearl River, Huaihe River, Liaohe River and Songhua River. Most rivers are located in Eastern and Southern China, and a few rivers are located in Northwest China. The ET gradually decreases from Southeast to Northwest China. The distribution of precipitation decreases sharply from the Southeastern (>3000 mm) to the Northwestern region (<50 mm) [30]. The occurrence of droughts and floods has become more frequent, and the intensity of drought and floods has also increased [31]. According to the climate type, MC can be divided into five parts (Figure 1), namely temperate monsoon climate (I), subtropical monsoon climate (II), plateau mountain climate (III), temperate continental climate (IV) and tropical monsoon climate (V). As the area of region V is relatively small, it is not suitable for GRACE detection; we focus on regions I-IV in this study.

GRACE TWSC
The GRACE RL06 monthly spherical harmonic (SH) coefficients product (truncated to degree and order 60) was provided by the Center for Space Research at the University of Texas at Austin, and was used to calculate TWSCs in MC for the period from April 2002 to December 2016.
The GRACE data were preprocessed as follows: C20 coefficients were replaced with those derived by satellite laser ranging [32]. Degree-1 coefficients were replaced using Swenson's results [33]. Filter processing combining a 300 km fan filter [34] and a PM36 de-correlation filter [35] was performed to weaken high-frequency and correlated errors. Due to the influence of order truncation and filter processing, there are leakage errors in the hydrological signals derived from GRACE data. The single scale factor method was used to calibrate GRACE-based TWSC results to restore loss signals [36].

GLDAS Model
The GLDAS is a global high-resolution land surface model published jointly by the Goddard Space Flight Center at NASA and the National Centers for Environmental Prediction at the National Oceanic and Atmospheric Administration (NOAA). It incorporates space-and ground-based observation and uses data assimilation techniques [37]. The monthly soil moisture (SM) and runoff data with a 1 • × 1 • spatial resolution were provided by three GLDAS-2.1 models (Noah, the variable infiltration capacity model and the catchment land surface model). The SM and runoff data were the average of these three models' data, and the time span was from April 2002 to December 2016.

In Situ Precipitation (PPT) Data
Monthly gridded precipitation data for the time period between April 2002 and December 2016 provided by the China National Meteorological Science Data Center and sorted by the National Meteorological Information Center with spatial resolution 0.5 • × 0.5 • were used for the analysis.

ET Data
ET is estimated according to a water balance equation [38,39]. Its expression is as follows: where P is PPT, R is the averaged runoff derived from GLDAS-2.1, and TWSC is derived from GRACE data.

Self-Calibrating Palmer Drought Severity Index (SCPDSI) Data
The SCPDSI data [40], a meteorological drought index, can evaluate the water loss caused by the imbalance of surface water supply and demand [41,42], and is provided by the Climate Research Unit at University of East Anglia. In this study, we extracted the relevant gridded data from April 2002 to December 2016 with spatial resolution 0.5 • × 0.5 • in MC. The severity of drought events can be classified as shown in Table 1 [43].

Extreme Weather Index Data
The ENSO is an abnormal phenomenon characterized by ocean surface warming or higher sea surface temperatures occurring in the Equatorial Eastern and Middle Pacific, and is able to influence the global atmospheric circulation, causing abnormal temperature and PPT [44,45]. The monthly Niño 3.4 index data indicate the magnitude of ENSO, which is provided by the NOAA. An El Niño event is designated as the occurrence of an ENSO index greater than or equal to 0.5 for 5 consecutive months, while a La Niña event is defined as an ENSO index less than or equal to −0.5 for 5 consecutive months [44,46].
The NAO is a kind of atmospheric circulation change that occurs in the middle and high latitudes of the Northern Hemisphere in winter, which reflects the atmospheric mass changes between Iceland Depression and Azores High in the North Atlantic. The NAO index can reflect the changes in the Iceland Depression and Azores High [47], and is also provided by the NOAA.

Calculation of GRACE-DSI Data
Based on the gridded GRACE TWSCAs estimated in Section 3.1, GRACE-DSI is the standardized GRACE-based TWSC as follows [26]: where TWSC i,j is TWSC in the ith year and jth month. i is a specific year from 2002 to 2016, and j is a specific month from January to December. TWSC mean j and σ j are the average and standard deviation of the TWSC in month j, respectively. This index can be used to detect drought and abnormally wet events. According to the size of GRACE-DSI values, drought events can be classified as shown in Table 2 [26]. Due to the truncation degree and filtering effect, the spatial resolution of GRACE-DSI grid data is 350 km [48].

The Extraction of Anomaly Signal
To discuss the relationship between GRACE-DSI and hydrological components, it is necessary to extract the anomaly signal from the original signal of each hydrological component. The time series of the original data can be decomposed into a long-term trend change term, seasonal term and anomaly term. The expression is as follows [36]: where Data(t) is the original data; t is the time; ε is the residual signal; and a 0 , a 1 , a 2 , a 3 , a 4 , a 5 are the pending parameters. a 0 is a constant; a 1 is the long-term trend change term; and a 2 , a 3 , a 4 , a 5 are the seasonal signals. Therefore, the expression of the anomaly signals is: Data anomaly (t) = Data(t) − [a 1 t + a 2 cos(2πt) + a 3 sin(2πt) + a 4 cos(4πt) + a 5 sin(4πt)] (4)

Comparison of GRACE-DSI and SCPDSI
To verify the drought detection ability of GRACE-DSI, we compared the temporal and spatial distribution of GRACE-DSI and SCPDSI (Figures 2 and 3). From Figure 2, it can be seen that the two drought indices show good consistency in general, with a correlation of 0.66 (Table 3). There were four episodes with long-term negative GRACE-DSI. The  Figure 3 shows the spatial distribution of GRACE-DSI and SCPDSI from October 2009 to September 2010. It can be seen in Figure 3 that the two drought indices had similar spatial distribution. In October 2009, most parts of MC were in the dry state, and only parts of Qinghai Province and the northern part of Northeast China were in the humid state. From November 2009 to January 2010, the arid regions were gradually decreasing, and the degree of dryness was also decreasing. January 2010 was the period with the fewest arid regions. By February 2010, the arid regions suddenly expanded, which was mainly concentrated in Southwest China, eastern Xinjiang, southern Northeast China and northern North China. The humid regions were concentrated in Qinghai Province, Northeast China and the Southeast Coastal region. Subsequently, the arid regions gradually decreased and the humid regions gradually increased. This trend continued until September 2010. The temporal and spatial changes of GRACE-DSI and SCPDSI in Southwest China are consistent with the severe drought event that occurred in the same period in the region [19].
From the perspective of different climate types, the highest correlation (0.72) between GRACE-DSI and SCPDSI was found in region II under a subtropical monsoon climate (see Table 3). In this region, the precipitation accounted for a relatively large proportion of the entire water cycle. The lowest correlation (0.29) was found in region III under the plateau mountain climate, implying a complex mechanism of droughts in this region. Since vegetation, snow and other terrestrial surface hydrological components are not explicitly processed in the SCPDSI [40], the correlations (0.42) between the two drought indices were the same in the other three regions. By comparing the temporal and spatial distribution of two drought indices and their correlation coefficients (Table 3, Figures 2 and 3), it can be seen that GRACE-DSI could detect the drought events.

The Analysis of Factors Affecting GRACE-DSI
To study the impact of SM, PPT, ET and runoff on drought events, we calculated the correlation coefficients between GRACE-DSI/SCPDSI and SM, PPT, ET and runoff anomaly. The anomaly signals were extracted according to Equation (4). For a more intuitive comparison and analysis, the following will elaborate on different climate regions. As the tropical monsoon climate region is very small, it is not conducive to the detection of GRACE satellites; therefore, it is considered here. Figure 4 shows the time series of GRACE-DSI and SM, PPT, ET and runoff anomaly. The SM and runoff had a relatively consistent change trend with GRACE-DSI and the consistency of SM with GRACE-DSI was higher. However, the time series of GRACE-DSI and SM had opposite change trends. This is also supported by the correlation coefficient results ( Table 4). The above results indicate that the SM is the most important factor affecting the GRACE-DSI. This is consistent with the definition of hydrological droughtthat is, an imbalance between the supply and demand of soil water storage causes a drought event [49].

Temperate Monsoon Climate (Region I)
Water 2021, 13, x FOR PEER REVIEW 9 of 24

The Analysis of Factors Affecting GRACE-DSI
To study the impact of SM, PPT, ET and runoff on drought events, we calculated the correlation coefficients between GRACE-DSI/SCPDSI and SM, PPT, ET and runoff anomaly. The anomaly signals were extracted according to Equation (4). For a more intuitive comparison and analysis, the following will elaborate on different climate regions. As the tropical monsoon climate region is very small, it is not conducive to the detection of GRACE satellites; therefore, it is considered here. Figure 4 shows the time series of GRACE-DSI and SM, PPT, ET and runoff anomaly. The SM and runoff had a relatively consistent change trend with GRACE-DSI and the consistency of SM with GRACE-DSI was higher. However, the time series of GRACE-DSI and SM had opposite change trends. This is also supported by the correlation coefficient results ( Table 4). The above results indicate that the SM is the most important factor affecting the GRACE-DSI. This is consistent with the definition of hydrological droughtthat is, an imbalance between the supply and demand of soil water storage causes a drought event [49].  To analyze the interaction between the terrestrial hydrological components under the temperate monsoon climate, the correlation coefficients between SM, PPT, ET and runoff were calculated (Table 5). Table 5 shows that there was a strong correlation between PPT and runoff, and between runoff and SM, implying an obvious water transport channel  To analyze the interaction between the terrestrial hydrological components under the temperate monsoon climate, the correlation coefficients between SM, PPT, ET and runoff were calculated (Table 5). Table 5 shows that there was a strong correlation between PPT Water 2021, 13, 2575 9 of 23 and runoff, and between runoff and SM, implying an obvious water transport channel between PPT, SM and runoff-that is, PPT affects runoff, runoff affects SM. Considering this with Table 4, we can see that the connection between PPT and GRACE-DSI is not strong, but PPT exerts the effect on GRACE-DSI through runoff and SM. Considering this alongside the strong correlation between ET and GRACE-DSI, it explains that PPT and ET are the main factors affecting the occurrence of drought under this climate. Since the correlation coefficient between SM and GRACE-DSI was larger than that between ET and GRACE-DSI, it can be said that PPT has a larger impact on drought than ET. In a word, PPT is the mainstay and ET is the supplement during the formation of drought events. Usually, PPT and ET are vulnerable to extreme weather, so it is necessary to consider the impact of extreme weather on drought events. Figures 5 and 6 show the performance of the time series of GRACE-DSI, PPT and ET during the ENSO and NAO events, respectively. From Figure 6, a total of five El Niño events and five La Niña events occurred during the study period. The five El Niño events caused abnormal decreases in PPT, while three El Niño events also led to abnormal increases in ET. Previous studies [50,51] indicate that because of the abnormal decrease of sea surface temperature in the Western Pacific (El Niño event), the East Asian summer monsoon weakened, causing the Western Pacific Subtropical High and rain belt to move southward, resulting in less PPT and higher temperatures in Northern China. The results in this paper provide scientific support for these results. Due to less PPT and more ET, severe drought events occurring during the El Niño events from June 2002 to February 2003 and from April 2015 to April 2016. While the two El Niño events from July 2004 to January 2005 and from September 2006 to January 2007 caused an abnormal decrease in PPT, the ET also showed an abnormal decrease. The drought did not occur under the mutual offset of PPT and ET. The El Niño events from July 2009 to March 2010 led to less PPT and more ET, but perhaps because of the degree of PPT reduction and the minor increase in ET, the combined effect was not enough to cause a drought. According to the above analysis, we found that an El Niño event can indeed cause an abnormally low PPT and an increase in ET, but its impact on PPT is slightly greater than that on ET. Whether an El Niño event will cause drought is the result of its combined effect on PPT and ET. The intensity of this effect needs to reach a certain level in order to induce drought.

Temperate Monsoon Climate (Region Ⅰ)
Three La Niña events led to an abnormal increase in PPT, and two La Niña events caused an abnormal reduction in ET. Ma [52] indicates that when a La Niña event occurs, the effects are simply the opposite of an El Niño event. At the time, the Western Pacific Subtropical High and rain belt moved northward with the strengthening of the East Asian Monsoon. Northern China showed higher PPT and higher temperature. It can be seen from Figure 6 that the La Niña event mainly affected PPT. Drought events occurred during the La Niña events from July 2007 to June 2008 and from August 2016 to December 2016. According to the previous studies, the cause of drought from 2007 to 2008 was that the northern part of China was in an interdecadal climate with high temperature and low PPT at that time, which caused a high probability of drought in Northeast China and North China [53]. The other drought event was the result of the interaction between the abnormal high pressure in Baikal Lake and Central Siberia and the abnormal low pressures in North China [54]. Due to the interaction of the above-mentioned high and low pressure, the central and eastern regions of China were controlled by the extremely strong dry and cold air flow. Under the control of the air flow, the water vapor transport was reduced, which in turn caused an abnormal decrease in PPT. This explains that the two drought events above had little to do with the La Niña events.
Under the temperate monsoon climate, the ENSO cycle mainly affects the location of the Western Pacific Subtropical High through the strength of the East Asian Monsoon. The location of Western Pacific Subtropical High determines the amount of PPT and the temperature. The temperature affects the amount of ET. Less PPT and more ET will increase the probability of drought during an El Niño event. The situation is the opposite during a La Niña event.
Water 2021, 13, x FOR PEER REVIEW 12 of 24 from October 2008 to February 2009 was affected by an El Niño event, indicating that the impact of the El Niño event may have exceeded the positive NAO event. However, the specific formation mechanism of the above drought event is relatively complicated, and conclusions cannot be drawn regarding this as of yet. The drought event from August 2011 to February 2012 was mainly caused by abnormal atmospheric circulation. At the same time, the Western Pacific Subtropical High was located to the south, which was not conducive to the transportation of water vapor. In addition, there was a strong sinking movement and low humidity in this region, which is conducive to the development and continuation of drought [56]. Ineson et al. and Graf et al. [57][58] indicate that ENSO signal is propagated to the stratosphere by upward movement, and then transmitted to the North Atlantic region through the "subtropical bridge" mechanism in the stratosphere, which causes the NAO response. El Niño is a negative NAO event, while La Niña is a positive NAO event. This is consistent with the significant negative correlation (−0.88) between ENSO and NAO indices in this paper. Comparing Figures 5 and 6, we can see that there were seven ENSO events accompanied by NAO events. Among the seven events mentioned above, there were three El Niño events, and the negative NAO events occurred at the same time; additionally, four La Niña events and positive NAO events occurred together. The above results provide strong data support for Chen et al. [59]. However, there were three ENSO events that did not cause corresponding NAO events. This may be because these three The relationship between NAO and GRACE-DSI, PPT and ET were analyzed in this paper (see Figure 5). There were three negative NAO events and seven positive NAO events during the study period. We found that the drought events occurred during the negative NAO events, and they were all caused by less PPT and more ET. Wu et al. [55] indicate that the NAO index has an inverse relationship with the range of Siberian High. When the NAO index was abnormally low, the Siberian High enhanced and its impact scope expanded, which cause the rain belt to move southward and there was less PPT and more ET in Northern China. This coincides with the results in this paper.
When the NAO index was abnormally high, the situation was the opposite. However, from Figure 5, it can be observed that there were three drought events during the positive NAO events. Among them, the drought event from August 2007 to August 2008 was caused by the background of climate, as explained in the previous section, and the one from October 2008 to February 2009 was affected by an El Niño event, indicating that the impact of the El Niño event may have exceeded the positive NAO event. However, the specific formation mechanism of the above drought event is relatively complicated, and conclusions cannot be drawn regarding this as of yet. The drought event from August 2011 to February 2012 was mainly caused by abnormal atmospheric circulation. At the same time, the Western Pacific Subtropical High was located to the south, which was not conducive to the transportation of water vapor. In addition, there was a strong sinking movement and low humidity in this region, which is conducive to the development and continuation of drought [56]. dry and cold air flow. Under the control of the air flow, the water vapor transport was reduced, which in turn caused an abnormal decrease in PPT. This explains that the two drought events above had little to do with the La Niña events.
Under the temperate monsoon climate, the ENSO cycle mainly affects the location of the Western Pacific Subtropical High through the strength of the East Asian Monsoon. The location of Western Pacific Subtropical High determines the amount of PPT and the temperature. The temperature affects the amount of ET. Less PPT and more ET will increase the probability of drought during an El Niño event. The situation is the opposite during a La Niña event. The relationship between NAO and GRACE-DSI, PPT and ET were analyzed in this paper (see Figure 6). There were three negative NAO events and seven positive NAO events during the study period. We found that the drought events occurred during the negative NAO events, and they were all caused by less PPT and more ET. Wu et al. [55] indicate that the NAO index has an inverse relationship with the range of Siberian High. When the NAO index was abnormally low, the Siberian High enhanced and its impact scope expanded, which cause the rain belt to move southward and there was less PPT and more ET in Northern China. This coincides with the results in this paper.
When the NAO index was abnormally high, the situation was the opposite. However, from Figure 6, it can be observed that there were three drought events during the positive NAO events. Among them, the drought event from August 2007 to August 2008 was caused by the background of climate, as explained in the previous section, and the one Ineson et al. and Graf et al. [57,58] indicate that ENSO signal is propagated to the stratosphere by upward movement, and then transmitted to the North Atlantic region through the "subtropical bridge" mechanism in the stratosphere, which causes the NAO response. El Niño is a negative NAO event, while La Niña is a positive NAO event. This is consistent with the significant negative correlation (−0.88) between ENSO and NAO indices in this paper. Comparing Figures 5 and 6, we can see that there were seven ENSO events accompanied by NAO events. Among the seven events mentioned above, there were three El Niño events, and the negative NAO events occurred at the same time; additionally, four La Niña events and positive NAO events occurred together. The above results provide strong data support for Chen et al. [59]. However, there were three ENSO events that did not cause corresponding NAO events. This may be because these three ENSO events were not Central Pacific (CP) events. According to the study results of Zhang et al. [60], there is a significant relationship between CP ENSO events and NAO events. Figure 7 shows the time series of GRACE-DSI, PPT, SM, runoff and ET anomaly under the subtropical monsoon climate. We found that GRACE-DSI, SM and runoff had similar change trends, which was also confirmed by the correlation coefficient results (Table 6). Unlike the results under the temperate monsoon climate, the correlation coefficients indicate that there was no significant correlation between GRACE-DSI and ET, but GRACE-DSI had a close connection with runoff. This may be related to the sufficient PPT and numerous rivers in this region. We calculated the correlation coefficients between the four hydrological components (Table 7). It can be seen that the way in which PPT affected SM was the same as in the temperate monsoon climate, and the impacts of PPT on runoff, and of runoff on SM, were much greater than was observed in the temperate monsoon climate. This indicates that PPT plays a leading role in drought events, and the impact of ET is small under the subtropical monsoon climate. ENSO events were not Central Pacific (CP) events. According to the study results of Zhang et al. [60], there is a significant relationship between CP ENSO events and NAO events. Figure 7 shows the time series of GRACE-DSI, PPT, SM, runoff and ET anomaly under the subtropical monsoon climate. We found that GRACE-DSI, SM and runoff had similar change trends, which was also confirmed by the correlation coefficient results (Table  6). Unlike the results under the temperate monsoon climate, the correlation coefficients indicate that there was no significant correlation between GRACE-DSI and ET, but GRACE-DSI had a close connection with runoff. This may be related to the sufficient PPT and numerous rivers in this region. We calculated the correlation coefficients between the four hydrological components (Table 7). It can be seen that the way in which PPT affected SM was the same as in the temperate monsoon climate, and the impacts of PPT on runoff, and of runoff on SM, were much greater than was observed in the temperate monsoon climate. This indicates that PPT plays a leading role in drought events, and the impact of ET is small under the subtropical monsoon climate.    Similarly, to discuss the influence of extreme climate on drought events and PPT, the change condition of GRACE-DSI and PPT anomaly time series during ENSO and NAO events are shown in Figures 8 and 9. Figure 8 shows that there were drought events during three La Niña events. These drought events were caused by low PPT. Previous studies have shown that the PPT in Southeastern China was greater than normal due to the southward shift of the PPT belt during La Niña events, while the situation was the opposite during the El Niño events [50,61]. There was no reduction in PPT during the other two La Niña events, and so drought events did not appear. This can be attributed to the large amount of PPT brought by typhoons [62]. However, drought events occurred during three El Niño events, and these three drought events occurred from July 2004 to January 2005, from September 2006 to January 2007 and from July 2009 to May 2010. The first drought event was mainly due to a lack of PPT caused by a lack of tropical cyclones [63]. The second drought event was mainly caused by the control of the Subtropical High in Southern China, and the increase and continuation of the Subtropical High in 2006 were closely related to the strengthening of atmospheric convection in the South China Sea and the abnormal heating field in the Bengal Bay [64]. The main reason for the drought from 2009 to 2010 was abnormal circulation. The Western Pacific Subtropical High was stronger than usual, and as a result the Indian Ocean water vapor was not transported to Southwest China. Therefore, there was less PPT in the region [65]. Similarly, to discuss the influence of extreme climate on drought events and PPT, the change condition of GRACE-DSI and PPT anomaly time series during ENSO and NAO events are shown in Figures 8 and 9. Figure 8 shows that there were drought events during three La Niña events. These drought events were caused by low PPT. Previous studies have shown that the PPT in Southeastern China was greater than normal due to the southward shift of the PPT belt during La Niña events, while the situation was the opposite during the El Niño events [50,61]. There was no reduction in PPT during the other two La Niña events, and so drought events did not appear. This can be attributed to the large amount of PPT brought by typhoons [62]. However, drought events occurred during three El Niño events, and these three drought events occurred from July 2004 to January 2005, from September 2006 to January 2007 and from July 2009 to May 2010. The first drought event was mainly due to a lack of PPT caused by a lack of tropical cyclones [63]. The second drought event was mainly caused by the control of the Subtropical High in Southern China, and the increase and continuation of the Subtropical High in 2006 were closely related to the strengthening of atmospheric convection in the South China Sea and the abnormal heating field in the Bengal Bay [64]. The main reason for the drought from 2009 to 2010 was abnormal circulation. The Western Pacific Subtropical High was stronger than usual, and as a result the Indian Ocean water vapor was not transported to Southwest China. Therefore, there was less PPT in the region [65]. From Figure 9, it can be observed that the drought events occurred during four positive NAO events, which were caused by low PPT. A previous study indicates that a positive NAO event caused the rain belt to move north, so there was less PPT in this region [55]. The positive NAO event from October 2008 to February 2009 caused a reduction in PPT, but the peak and average values of GRACE-DSI were greater than 0 because of two large-scale waves in the winter 2008, which made the temperature lower than usual [66]. Therefore, severe drought did not appear under the interaction of reduced PPT and low temperature. There was only one drought during a negative NAO event from October From Figure 9, it can be observed that the drought events occurred during four positive NAO events, which were caused by low PPT. A previous study indicates that a positive NAO event caused the rain belt to move north, so there was less PPT in this region [55]. The positive NAO event from October 2008 to February 2009 caused a reduction in PPT, but the peak and average values of GRACE-DSI were greater than 0 because of two large-scale waves in the winter 2008, which made the temperature lower than usual [66]. Therefore, severe drought did not appear under the interaction of reduced PPT and low temperature. There was only one drought during a negative NAO event from October 2009 to March 2010, which was caused by the Western Pacific Subtropical High. The specific reason is explained in the previous paragraph.

Plateau Mountain Climate (III)
The time series of GRACE-DSI, PPT, SM, ET and runoff anomaly under the plateau mountain climate are shown in Figure 10. Only the ET and GRACE-DSI had a significant correlation, and it was negative. This is also confirmed by the results in Table 8. The change trends of runoff and PPT anomaly were relatively smooth. Table 9 shows that the correlations between the four hydrological components (SM, PPT, SM and runoff) were not strong, indicating that ET is the main factor causing drought events under the plateau mountain climate. Figures 11 and 12 show the time series of GRACE-DSI and ET anomaly during ENSO and NAO events. Figure 11 shows that drought events occurred during all five El Niño events. There were four drought events caused by higher ET. Xu et al. indicated that ENSO events affect the surface temperature of the Tibetan Plateau by adjusting the strength of the Indian Ocean Monsoon [67]. In an El Niño year, the Indian Ocean Monsoon weakens and the surface temperature rises, leading to an increase in the possibility of drought events. In La Niña years, the opposite is true. However, drought events occurred during two La Niña events. The first drought event from July 2007 to June 2008 was due to a stronger Middle East Subtropical High. The warm and humid airflow from Bengal Bay could not reach the Tibetan Plateau under the control of this high [68]. The second drought from August to December 2016 was caused by the southward movement of an abnormal continental warm high and the northward movement of the Western Pacific Subtropical High. Under the influence of the above two high pressures, there was long-term, large-scale, sunny and hot weather in this region [54].   Figure 12 shows that drought events occurred during all three negative NAO events, resulting from the southward movement of the Siberian High [55]. The opposite was true during positive NAO events. However, the drought event from April 2007 to April 2008 appeared during a positive NAO event. The reason for this drought was explained in the previous paragraph.   Figure 12 shows that drought events occurred during all three negative NAO events, resulting from the southward movement of the Siberian High [55]. The opposite was true during positive NAO events. However, the drought event from April 2007 to April 2008 appeared during a positive NAO event. The reason for this drought was explained in the previous paragraph.   Figure 13 compares the time series of GRACE-DSI and PPT, SM, ET and runoff anomaly under the temperate continental climate. We found that the GRACE-DSI had a significant correlation with both ET and SM. Additionally, the change trend of the PPT anomaly was relatively stable. The absolute value of the correlation coefficient between ET and GRACE-DSI (0.76) was greater than that of GRACE-DSI and PPT (0.57) (Table 10). This   Figure 13 compares the time series of GRACE-DSI and PPT, SM, ET and runoff anomaly under the temperate continental climate. We found that the GRACE-DSI had a significant correlation with both ET and SM. Additionally, the change trend of the PPT anomaly was relatively stable. The absolute value of the correlation coefficient between ET and GRACE-DSI (0.76) was greater than that of GRACE-DSI and PPT (0.57) (Table 10). This  Figure 13 compares the time series of GRACE-DSI and PPT, SM, ET and runoff anomaly under the temperate continental climate. We found that the GRACE-DSI had a significant correlation with both ET and SM. Additionally, the change trend of the PPT anomaly was relatively stable. The absolute value of the correlation coefficient between ET and GRACE-DSI (0.76) was greater than that of GRACE-DSI and PPT (0.57) (Table 10). This indicates that the impact of ET on drought events is greater than that of PPT in the temperate continental climate in this climate. We calculated the correlation coefficients between the four hydrological components (SM, ET, PPT and runoff), as shown in Table 11. The results also show the same relationships between PPT, runoff and SM.
indicates that the impact of ET on drought events is greater than that of PPT in the temperate continental climate in this climate. We calculated the correlation coefficients between the four hydrological components (SM, ET, PPT and runoff), as shown in Table 11. The results also show the same relationships between PPT, runoff and SM.   Figures 14 and 15. Figure 14 shows that drought events occurred during three El Niño events. The previous studies show that the influence of ENSO events in the temperate continental climate region is basically the same as that in the temperate monsoon climate region [50][51]. However, there was no drought during the other two El Niño events, because the degree of anomalous decrease in PPT was small and the ET was less than usual. Drought events occurred during two La Niña events. The Subtropical High that moved eastward from Western or Central Asia under the influence of atmospheric circulation controlled this region and was the main factor causing the drought event from  The time series of GRACE-DSI, PPT and ET anomaly during ENSO and NAO events are shown in Figures 14 and 15. Figure 14 shows that drought events occurred during three El Niño events. The previous studies show that the influence of ENSO events in the temperate continental climate region is basically the same as that in the temperate monsoon climate region [50,51]. However, there was no drought during the other two El Niño events, because the degree of anomalous decrease in PPT was small and the ET was less than usual. Drought events occurred during two La Niña events. The Subtropical High that moved eastward from Western or Central Asia under the influence of atmospheric circulation controlled this region and was the main factor causing the drought event from July 2007 to June 2008 [69]. The drought event from August 2011 to April 2012 was mainly caused by abnormal atmospheric circulation. As the Subtropical High was weaker than usual, the region was mainly controlled by cold air, and warm and humid air currents could not reach the area. This led to reduced PPT and drought [56]. July 2007 to June 2008 [69]. The drought event from August 2011 to April 2012 was mainly caused by abnormal atmospheric circulation. As the Subtropical High was weaker than usual, the region was mainly controlled by cold air, and warm and humid air currents could not reach the area. This led to reduced PPT and drought [56]. From Figure 15, two drought events appeared during negative NAO events. Although the PPT was less than usual, ET was also less; this is why there was no drought during the negative event from August 2008 to March 2003. Drought only appeared during two positive NAO events. The drought from July 2015 and April 2016 was affected by El Niño events. As the region was affected by the Subtropical High, the cold air flowed southward. At the same time, due to the influence of the sinking airflow, the transportation of water vapor in the south was blocked. This caused a drought event from October 2008 to February 2009 [70]. From Figure 15, two drought events appeared during negative NAO events. Although the PPT was less than usual, ET was also less; this is why there was no drought during the negative event from August 2008 to March 2003. Drought only appeared during two positive NAO events. The drought from July 2015 and April 2016 was affected by El Niño events. As the region was affected by the Subtropical High, the cold air flowed southward. At the same time, due to the influence of the sinking airflow, the transportation of water vapor in the south was blocked. This caused a drought event from October 2008 to February 2009 [70]. Water 2021, 13, x FOR PEER REVIEW 20 of 24 Figure 15. GRACE-DSI, PPT and ET anomaly compared with the NAO index.

Discussion
PPT and ET are the two decisive factors leading to drought events. Extreme climate is one of the factors causing abnormal PPT and ET. The purpose of our study is to discuss the influence of extreme climate on GRACE-DSI, PPT and ET in different climate regions. According to the results in Section 4, extreme climate led to drought by affecting PPT and temperature. Table 12 shows relationship between extreme climate and both PPT and ET anomaly in four climate types. From Table 12, it can be seen that ENSO index was negatively correlated with PPT anomaly under the temperate continental climate and temperate monsoon climate. Meanwhile, there was a positive correlation between ENSO index and PPT anomaly under the subtropical monsoon climate (see the analysis in Section 4). The previous studies show that East Asian Monsoon weakens during El Niño events, which causes the Western Pacific Subtropical High to weaken and move to the south. When this occurs, the boundary between cold air and warm and humid air in MC moves further southward than in regular years. This increases the probability of excessive PPT in South China, while it is likely to lead to reduced PPT in North China. During La Niña events, the situation is the opposite [51,61,71].

Discussion
PPT and ET are the two decisive factors leading to drought events. Extreme climate is one of the factors causing abnormal PPT and ET. The purpose of our study is to discuss the influence of extreme climate on GRACE-DSI, PPT and ET in different climate regions. According to the results in Section 4, extreme climate led to drought by affecting PPT and temperature. Table 12 shows relationship between extreme climate and both PPT and ET anomaly in four climate types. From Table 12, it can be seen that ENSO index was negatively correlated with PPT anomaly under the temperate continental climate and temperate monsoon climate. Meanwhile, there was a positive correlation between ENSO index and PPT anomaly under the subtropical monsoon climate (see the analysis in Section 4). The previous studies show that East Asian Monsoon weakens during El Niño events, which causes the Western Pacific Subtropical High to weaken and move to the south. When this occurs, the boundary between cold air and warm and humid air in MC moves further southward than in regular years. This increases the probability of excessive PPT in South China, while it is likely to lead to reduced PPT in North China. During La Niña events, the situation is the opposite [51,61,71].  Table 12 shows that ENSO index and ET anomaly were positively correlated in the temperate continental climate and temperate monsoon climate. Zuo indicated that the influence of ENSO events on the temperature in China is mainly concentrated in North China. The average maximum temperature was observed to be higher in the summer and autumn than normal during El Niño events, while one was lower during La Niña events [66]. Abnormal temperature changes are directly reflected in the ET. The PPT and ET are the two important hydrological components in the terrestrial water cycle. Therefore, abnormal changes in PPT and ET lead to abnormalities in TWSC, which in turn affects the GRACE-DSI. According to Table 12, the impact of extreme climate on PPT was greater than that on ET.
Due to the geographical location, the climate of the plateau mountain climate region is more strongly affected by the Indian Ocean Monsoon. Although blocked by the Himalaya, the Indian Ocean Monsoon still brings some warm and humid air to this region. In El Niño years, the surface temperature of the Qinghai-Tibet Plateau is relatively high due to the weakening of the Indian Ocean Monsoon, resulting in an increase in ET. In La Niña years, there is less ET in this region [65]. Therefore, the results in Table 12 also confirm the above conclusion. As the region with temperature continental climate is located in the hinterland of the continent and far from the ocean, it is difficult for warm humid air to reach this region, causing the region to be mostly semi-arid and arid. Therefore, the biggest difference between this region and the temperature monsoon climate is that ET plays a key role in the entire terrestrial water cycle.

Conclusions
To discuss the cause of drought events under different climatic conditions, we took four climatic regions in MC as an example to study the influences of various hydrological components and extreme climate on drought events using GRACE-DSI data. Firstly, we compared the temporal and spatial distribution of GRACE-DSI and SCPDSI and calculated the correlation coefficient between these two indices. The results indicate that GRACE-DSI and SCPDSI have similar temporal and spatial distribution and are strongly correlated (0.66) in MC. This proves that GRACE-DSI can detect drought events in MC. Secondly, we studied the influence of drought-related factors and extreme climate on drought events by analyzing the relationship between GRACE-DSI and anomalous changes of SM, PPT, ET and runoff during ENSO and NAO events. The results show that PPT and ET are the main causative factors of drought events. However, they play different roles in different climate regions. PPT plays a major role in temperate monsoon climate and subtropical monsoon climate regions, while ET plays a dominant role in the other two climate regions. ENSO and NAO events first affected the monsoon and regional high, and changes in the monsoon and regional high directly affected the changes of PPT and temperature in a given region. The changes of temperature affected the amount of ET. Therefore, extreme climate has a very important influence on regional drought events. However, the influence of extreme climate on different climatic regions is different depending on the different geographical locations and influence mechanisms.
Our research helps to further reveal the causes of drought events, and provides a reference for drought research in other similar and different climate regions. We mainly focused on the influence of climatic factors on drought events in this paper. Therefore, future work will mainly study the influence of human factors on drought events and the