As global warming continues, extreme precipitation events occur frequently in inland areas, seriously affecting human security and the ecological environment. Spatiotemporal evolution of extreme precipitation as well as response of extreme precipitation to climatic warming and its mechanism were investigated by considering the Weihe river basin in a monsoon transition zone of China as a research object. The results indicate that while the annual average temperature of the Weihe river basin increased with fluctuations from 1966 to 2017, except for the consecutive dry days (CDD) and simple daily intensity index that increased slightly, the other extreme precipitation indices (consecutive wet days, R25, and Rx5day) tended to decrease. Moreover, except for the CDD, the other four indices gradually increased from the northwest to the southeast, showing a similar trend to the temperature. The relationship between the 95th percentile threshold and temperature (hereinafter referred to as the P 95d–T relationship) in the Weihe river basin demonstrates the hook structure and its strength in terms of response is mainly dominated by the super-Clausius–Clapeyron (C–C) and C–C scaling. Furthermore, the peak temperature rises gradually from the northwest to the southeast. The results can provide important reference for the prediction of climate change and future studies of disaster risk in the Weihe river basin.
Extreme precipitation, as one of the most concerning and damaging natural disasters in the world, is a key research topic among those interested in climate change and disaster prevention. In recent decades, with increasingly serious global warming and the intensification of human activities, extreme precipitation events in inland areas are inclined to increase. Disasters, such as floods and mudslides, caused by extreme precipitation occur frequently, adversely affecting the ecological environment and socio-economic sustainable development. This makes it necessary to study the evolution of extreme precipitation and explore its response to climatic warming at a spatiotemporal scale.
On the global scale, the variation of extreme precipitation is complex and exhibits obvious regional characteristics , and particularly, the variation of extreme precipitation in the tropics is the largest . In recent years, increasingly more scholars have focused on analysis of spatiotemporal variation of extreme precipitation . For example, Ren et al.  revealed spatiotemporal variations of extreme precipitation in Jilin Province, China from 1961 to 2005 and found that extreme precipitation varies with altitude, longitude, and latitude. Based on Pearson correlation analysis and Mann–Kendall trend test, Ma et al.  studied the spatiotemporal distribution of ten extreme precipitation indices in the Haihe river basin, China, and correlations of these indices with atmospheric circulation.
In recent decades, the global climate has changed significantly, and the most prominent feature is that the global climate shows a warming trend . The frequency and intensity of extreme precipitation events increase significantly in the context of global warming . In the case of global warming, the response of extreme precipitation to temperature in China remains to be discussed. Trenberth et al.  found that the link between extreme precipitation and temperature is found in the amount of water vapor in the atmosphere. Ye et al.  pointed out that there is a stable and significant positive correlation between water vapor and precipitation, especially for the extreme precipitation intensity, whereas a Clausius–Clapeyron (C–C) relation is also shown between water vapor and temperature. The C–C relation describes variation characteristics of atmospheric saturated vapor pressure as a function of ambient temperature and ambient pressure and indicates that the water vapor storage capacity of the atmosphere increases at 7%/°C with the increase of the temperature . Therefore, this provides a physical basis for connecting the relationship between extreme precipitation and temperature. In the past 2 years, the research based on the numerical model shows that with the increase of the temperature, the rate of increase in extreme precipitation is consistent with C–C variability. However, the daily extreme precipitation in some areas remains variable, although exhibiting quasi-C–C scaling with increasing temperature, whereas in other areas, the growth in extreme precipitation intensity may even exceed that is manifested by C–C scaling . Meanwhile, extreme precipitation in most Chinese mainland areas generally presents a parabolic track (first increasing, then decreasing) with increasing temperature, namely the hook climate response structure . By using a coupled climate and hydrological model, Yin et al.  demonstrated that, with global warming, the hook structure gradually moves and as the peak temperature rises, the structure constantly moves to the higher temperature. Moreover, the peak value of the parabola moves upward, resulting in a significant increase in extreme values of precipitation and rainstorm runoff in the future and intensification of flood disasters. The hook structure is attributed to the limitation of continental moisture, which is generally considered as a secondary factor in the development of convective precipitation but is important in limiting the extreme intensity. Earlier studies suggest that the hook structure is simultaneously constrained by both thermal and dynamic environmental changes of the climate system ; therefore, thermodynamic and dynamic mechanisms of the response structure of extreme precipitation to climatic warming remain to be discussed.
The Weihe river is the mother river of the Guanzhong area (Shaanxi Province, China), and the variability of precipitation in its catchment area has a far-reaching impact on the Guanzhong area. In recent years, many flood disasters have occurred in the Weihe river basin. In view of this, based on daily meteorological data of the river basin from 1966 to 2017, extreme precipitation indices were obtained by using the ClimDex model to evaluate objectively the spatiotemporal evolutions of extreme precipitation and temperature in the basin in recent 52 years. Furthermore, the response of extreme precipitation to climatic warming and its mechanisms were further investigated, which provides a basis for predicting and exploring trends in extreme precipitation in the future.
2 Overview of the study area
The Weihe river with the total length of its main stream of 818 km, as the largest primary tributary of the Yellow river, originates from Niaoshu mountain in Gansu Province, China, and flows through Tianshui city (Gansu Province) and Xi’an city (Shaanxi Province). The Weihe river basin (Figure 1), located in the central China and the middle reaches of the Yellow river basin, has a total area of 134,800 km2 and belongs to continental monsoon climate, with the annual precipitation of 500–800 mm. Precipitation is mainly concentrated in summer, with a nonuniform spatiotemporal distribution and large seasonal differences, and the spatial distribution of multiyear average temperature is imbalanced (Figure 2). From 1966 to 2017, the annual average temperature varied from 2.00 to 15.68°C and gradually increased from the northwest to the southeast. The basin is an important grain producing area and industrial and commercial center in the northwest of China, and its water resources directly support 22 million people. With the construction of Guanzhong-Tianshui Economic Zone, the Weihe river basin will greatly promote the economic development of western China . In recent years, frequent floods and droughts in the basin have restricted the sustainable development of national economy in the region due to impacts of climate change and human activities [15,16]. Therefore, it is necessary to study the spatiotemporal evolution of extreme precipitation and its response relationship in the Weihe river basin.
3 Date and methods
Daily meteorological data from 17 national meteorological stations in the Weihe river basin from 1966 to 2017 were selected, including the daily precipitation, daily average temperature, and daily maximum and minimum temperatures. The data come from the National Meteorological Information Center of China (http://data.cma.cn/). The extreme precipitation index was calculated based on daily precipitation, and daily maximum and minimum temperature data using the ClimDex model.
3.2.1 ClimDex model
In this study, 5 of the 27 extreme precipitation indices recognized by the World Meteorological Organization (WMO) were selected as research objects (Table 1) [17,18,19,20,21], and the RClimDex software package (http://etccdi.pacificclimate.org/software.shtml) was used to calculate these indices . The extreme precipitation indices were obtained year-by-year. In this model, extreme precipitation indices were defined by daily precipitation and temperature data, and these indices have characteristics of low noise and strong significance . Data quality control is a prerequisite for index calculation. The RclimDex model was used for data quality control, including identifying errors and outliers. Finally, the calculation and processing of extreme precipitation were realized. The trends were fitted by utilizing the linear regression method to explore variations in each extreme precipitation index on the temporal scale.
|Extreme precipitation index||Index||Definition||Unit|
|CDD||Consecutive dry days||Maximum number of consecutive days with daily precipitation less than 1 mm||day|
|CWD||Consecutive wet days||Maximum number of consecutive days with daily precipitation larger than or equal to 1 mm||day|
|R25||Extreme precipitation days||Days with daily precipitation larger than or equal to 25 mm in each year||day|
|Rx5day||Maximum 5 day precipitation||Maximum precipitation for five consecutive days every year||mm|
|SDII||Simple daily intensity index||Ratio of total precipitation to total days with daily precipitation larger than or equal to 1 mm||mm/day|
3.2.2 Nonparametric Mann–Kendall test
The Mann–Kendall method, a widely used nonparametric test method, was used to elucidate the trend of extreme precipitation series in the Inner Mongolia Autonomous Region, China. This method does not need to follow a certain sample distribution and will not be disturbed by outliers. It is recommended by the WMO, widely used and suitable for nonnormally distributed data, such as meteorological and hydrological data, and it has the advantage of convenient calculation [24,25,26]. The principles of the method have been described in earlier studies [24,25,26]; if the influence of sequence correlation is detected in the time series, a prewhitening procedure is required to eliminate the influence of sequence correlation before the Mann–Kendall test is used [27,28].
3.2.3 Heuristic segmentation algorithm
Based on the idea of the sliding t-test, the heuristic segmentation algorithm, also known as the Backus–Gilbert algorithm, can divide a nonstationary series into several stationary subseries with different mean values, and each subseries represents different physical backgrounds. The scales of each decomposed average segment are variable and not constrained by the method itself, overcoming the problem whereby previous detection methods are based on stationary and linear processes; because the iterative algorithm of dividing one into two is adopted during segmentation, this method reduces the computational complexity and has good practicability [29,30]. Details of the specific calculation process can be found elsewhere [29,30].
3.2.4 Analytical method for the relationship between extreme precipitation and temperature
The C–C relationship is a method used to describe the variation of the pressure with the temperature in a single component system at phase equilibrium. It describes the variation characteristics of the saturated vapor pressure in the atmosphere as a function of ambient temperature and ambient pressure .
where, R, L, and e s represent the gas constant, latent heat of vaporization of water, and saturated vapor pressure corresponding to temperature T, respectively.
By selecting daily precipitation data P (mm) with P ≥ 0.1 mm and the corresponding daily average temperature T (°C), the whole temperature interval is divided by varying temperature intervals, leaving similar numbers of samples in each group. Based on this method, and due to the Provisions of China’s National Meteorological Administration, rainfall of 50 mm or more within 24 h was called a rainstorm: the 95th percentile thresholds of the samples in each station and each group were calculated and recorded as P 95d, corresponding to the average temperatures of the samples in each group. The hook structure was identified by using the locally weighted regression smoothing method, and the inflection point of the structure was obtained. The temperature at the inflection point was defined as the peak temperature. By using the exponential regression method, the P 95d logarithms before the peak temperature were linearly fitted by the least-squares method to determine the relationship between extreme precipitation and temperature .
where ΔT (°C), P 1 (mm), P 2 (mm), and p (%/°C) denote the differences in temperature, precipitation, precipitation at the previous temperature, precipitation at the latter temperature, and rate of change of P with respect to T.
When p ≈ 7%/°C, the relationship between extreme precipitation and temperature meets the C–C relation; because the C–C rate is not fixed but affected by other meteorological factors, (6∼8%)/°C is taken as the range of values meeting the C–C relationship in this study .
4.1 Spatiotemporal distribution characteristics of the annual average temperature
As shown in Figure 3, with global warming, the temperature of the Weihe river basin increased with fluctuations from 1966 to 2017, and the rate of increase in temperature was 0.35°C/10a. The temperature obtained by the heuristic segmentation algorithm suddenly changed in 1997. Through analysis, it is found that before the temperature suddenly changed, the temperature rose at 0.20°C/10a, which is higher than that after the aforementioned sudden change, indicating that the temperature of the Weihe river basin gradually increased after 1997, but at a slower rate.
4.2 Spatiotemporal distribution characteristics of extreme precipitation indices
4.2.1 Temporal variation characteristics of extreme precipitation indices
As displayed in Figure 4, extreme precipitation indices in the Weihe river basin have shown a gentle growth or decline trend in recent 52 years, but the interannual difference is large. With the continuous global warming, except for slight increase in the consecutive dry days (CDD) and simple daily intensity index (SDII), the others indices tend to decrease slightly. The overall change of the CDD is small, but the extreme value appeared in 1999, which may be related to the drought around 1999. Contrary to CDD, the number of consecutive wet days (CWD) is much smaller, and too small CWD contributes to the occurrence of drought, which decreased from 6 days in 1966 to 5 days in 2017. Although the changes are small, the interannual extreme value fluctuates more significantly. In addition, the maximum 5 day precipitation (Rx5day) decreases slowly, and the extreme precipitation days (R25) index reflecting days of heavy rain also changes (although slightly). The SDII slowly increases overall, suggesting that the precipitation intensity in the Weihe river basin tends to increase, with an average increase of 0.07 mm/day every 10 years, and the change range of interannual effective daily precipitation is small. The changes of extreme precipitation indices before and after the sudden change of the annual average temperature were further evaluated (Table 2). It is found that except for a downward trend in the CDD before and after the sudden change, the other extreme precipitation indices tend to reduce before, while increasing after, the sudden change in annual average temperature.
|Index||Before the sudden change of T||After the sudden change of T|
|CDD||y = 0.0308x − 4.5441, R 2 = 0.0006||y = −0.4531x + 968.61, R 2 = 0.0265|
|CWD||y = −0.0541x + 112.75, R 2 = 0.1482||y = 0.0242x − 43.3, R 2 = 0.019|
|R25||y = −0.0397x + 83.029, R 2 = 0.0957||y = 0.049x − 93.918, R 2 = 0.0712|
|Rx5day||y = −0.7164x + 1505.1, R 2 = 0.2371||y = 0.5234x − 962.9, R 2 = 0.0317|
|SDII||y = −0.0161x + 40.229, R 2 = 0.0394||y = 0.0324x − 56.37, R 2 = 0.0536|
In general, except for the CDD and SDII that slightly increase in terms of interannual variation, the other indices tend to reduce with a small amplitude in the Weihe river basin. Moreover, except for a decreasing trend in the CDD before and after the sudden change in temperature, the other indices decrease before while increasing after the sudden change. This indicates that, with global warming, extreme precipitation events in the Weihe river basin are also increasing in frequency and have a certain relationship with the temperature.
4.2.2 Spatial distribution characteristics of extreme precipitation indices
To explore multiyear spatial distribution of extreme precipitation in the Weihe river basin, the spatial distribution map of extreme precipitation indices was drawn (Figure 5): the CDD of the Weihe river basin gradually decreases from the northwest to the southeast. The lower values are distributed in Mount Hua Area, whereas the higher values are found in the northwest of the Weihe river basin, showing a significant difference. The spatial distribution of the CWD is opposite to that of the CDD and tends to increase from the northwest to the southeast. The minimum value appears in Yan’an city, whereas the maximum value is found in Foping Region in the south of the basin. The spatial distribution of R25 shows an increasing trend from the northwest to the southeast. The lower values are present in the area northwest to Changwu County and reach 2 day. The overall trends in Rx5day and SDII are consistent with that of R25; that is, high values are found in the southeast of the Weihe river and lower values are located in the northwest of the Weihe river basin.
Based on statistics relating to the trend in changes of extreme precipitation indices at meteorological stations, in the areas where the annual average temperature of the Weihe river basin is high, the CDD and SDII in most stations increase, whereas the CWD in the majority of stations reduces. Moreover, there is a small difference in stations with upward and downward trends of the R25 and Rx5day. In the areas with low annual average temperature of the Weihe river basin, the CWD in most stations shows a downward or significant downward trend, whereas the R25 and SDII increase at most stations. A small difference is found at stations with upward and downward trends of the CDD and Rx5day.
In conclusion, the extreme precipitation indices including CWD, R25, Rx5day, and SDII in the Weihe river basin tend to show gradual increases from the northwest to the southeast, which is similar to the trend in annual average temperature. The CDD shows the opposite, with a gradual decrease from the northwest to the southeast, which is similar to the elevation distribution in the Weihe river basin (Figure 1). Meanwhile, the Weihe river basin, located in the monsoon climate transition zone, is affected by both the monsoon circulation system and westerly circulation system and has a complex landform. For these reasons, the spatial distributions of extreme precipitation indices in the basin differ. From the perspective of trend, in areas with high and low temperatures in the basin, extreme precipitation indices have different trends. This further indicates that extreme precipitation responds to temperature in the Weihe river basin, which provides a basis for the following study.
4.3 Response of extreme precipitation to climatic warming
By analyzing P 95d–T relationship in the Weihe river basin, the relationship is shown to have the hook structure in the whole region of the basin. In other words, the daily extreme precipitation presents peak characteristics of first increasing, then falling with increasing temperature. This specifically manifests as an increase with temperature when the temperature is below the peak temperature and a decrease with the increase of the temperature when the temperature is above the peak temperature. The data of daily extreme precipitation before the peak temperature were exponentially fitted (Figure 6). Through analysis, it is found that the response intensity of the daily extreme precipitation to the temperature recorded at ten stations, such as Huajialing, Pingliang, Tianshui, Xiji, Wuqi, Yan'an, Xifeng, Fuping, Shang and Guyuan, is higher than that found by C–C scaling (i.e., super-C–C scaling prevails). The response intensity at six stations, such as Lintao, Minxian, Changwu, Wugong, Huan and Luochuan, confirms to the C–C relationship; however, only at one station (Mount Hua) is the response intensity lower than that when using C–C scaling (i.e., sub-C–C scaling prevails). This indicates that the response intensity of daily extreme precipitation to the climatic warming in the Weihe river basin is mainly dominated by super-C–C and C–C scaling. After comparing and analyzing the elevation distribution (Figure 1) and spatial distribution of temperature (Figure 2) in the Weihe river basin, 78% of stations in low-value areas of annual average temperature in the basin show the super-C–C scaling and a high elevation. However, in high-value areas, 50% of stations present C–C scaling, and 38% of stations show super-C–C scaling at a low elevation. Based on the spatial distribution, the response intensity of extreme precipitation to climatic warming has nonuniform spatial distribution characteristics. This is because the Weihe river basin is in the transition zone from a monsoon climate to a nonmonsoon climate. The climate in the basin has obvious dynamics, large fluctuation amplitude, spatial variability, and complex influencing factors, which have certain impacts on extreme precipitation and temperature. Through examination of the spatial distribution of the peak temperature in the basin, the peak temperature gradually increases from the northwest to the southeast, akin to the spatial distribution of the annual average temperature. Furthermore, the peak temperature ranges from 11.2 to 20°C.
The above research illustrates that the response intensity of the daily extreme precipitation to climatic warming in the Weihe river basin is dominated by super-C–C and C–C scaling. Due to influences of climate and landform, the response intensity presents a nonuniform spatial distribution. The peak temperature gradually increases from the northwest to the southeast, akin to the spatial distribution of the annual average temperature. This suggests that extreme precipitation is closely related to the temperature in the basin, and the response intensity between them shows super-C–C and C–C relationships due to effects of climate and landform.
Under the joint effects of global warming and urbanization, the temperatures across the Weihe river basin have tended to increase in the past 52 years. Although the temperature increases, the other extreme precipitation indices show a small decreasing trend except for the CDD and SDII that increase slightly. For agricultural production, the decline of extreme precipitation indices reduces the total amount of water resources and surface runoff to a certain extent, resulting in an increase in water consumption for crops requiring irrigation. This has an important impact on the development of agriculture and animal husbandry and people’s lives . In terms of spatial distribution characteristics, the temperature gradually increases from the northwest to the southeast, and the temperature change is closely related to the increasing urbanization occurring in the area. Gao et al.  found that urbanization and heat-island effects exert important influences on the temperature increasing trend on a regional or local scale. Therefore, the higher temperature in the southern cities, compared to the other northern stations in the Weihe river basin, may be related to a heat-island effect.
Furthermore, extreme precipitation indices in the Weihe river basin are nonuniformly distributed in spatial terms, which is the result of the joint action of climate and landform-related factors. The specific manifestations are as follows: the Weihe river basin has a continental monsoon climate and is in the transition zone from arid to humid areas. The Loess Plateau lies to the north, Qinling mountains lie to the south, and the basin is separated by the Liupan mountains in the east and west. Such climate and landform inevitably have significant effects on extreme precipitation and temperature across the basin.
Through analyzing the response of extreme precipitation to climatic warming in the Weihe river basin, it is found that the response is of the form of the hook structure and the response intensity is mainly dominated by super-C–C and C–C scaling, with nonuniform spatial distributions therein. The C–C relationship shows that, when the global average relative humidity remains unchanged, the atmospheric water vapor content increases with climatic warming at an exponential growth rate of (6–7)%/°C . However, at a higher temperature, the relative humidity does not remain stable due to limited atmospheric humidity . Therefore, the hook structure essentially breaks the concept of strict enhancement of the atmospheric humidity with climatic warming but shows a downward trend when the temperature is higher than the peak temperature . Yin et al.  demonstrated that the deviation from the pure thermodynamic scale is the key to predicting how extreme weather hazards will respond to anthropogenic climatic warming, and the hook structure is attributed to the limitation of continental moisture. Existing studies show that the response intensity of extreme precipitation in various regions of the world is generally quite different from that predicted by C–C scaling, and the spatial difference is also large in different regions. This is because the C–C equation can only characterize the thermodynamic factors of climate response of extreme precipitation, whereas the atmospheric dynamic mechanism also has an important impact on precipitation formation . From the point of view of thermodynamic mechanism, the temperature is mainly controlled by the thermal process. In other words, when the outside world is under radiation forcing, due to global warming, the capacity of the atmospheric boundary layers to contain water vapor increases and the atmospheric water vapor content increases, hence the intensity of extreme precipitation also increases . The change in precipitation is jointly affected by thermal and dynamic processes. Atmospheric dynamics can change response mechanisms of extreme events to the changing environment by affecting factors, such as large-scale circulation and advection . The Weihe river basin, located in the monsoon climate transition zone, is affected by both a monsoon circulation system and a westerly circulation system . Meanwhile, the basin has a landform that is typically high in the west and low in the east, with the Loess Plateau in the north and Qinling mountains in the south. Therefore, by combining with thermodynamic and dynamic mechanisms, climate and landform were found to affect the hook response intensity of extreme precipitation to climatic warming in the basin to some extent.
Based on daily meteorological data from 17 meteorological stations in the Weihe river basin recorded between 1966 and 2017, five extreme precipitation indices (including CDD, CWD, R25, Rx5day, and SDII) were calculated using the RClimDex model. By using the heuristic segmentation algorithm and nonparametric Mann–Kendall test, spatiotemporal evolutions of extreme precipitation indices in the Weihe river basin were quantitatively studied. Moreover, the response relationship and the mechanism between the daily extreme precipitation and climatic warming were also investigated. The following conclusions are drawn:
With global warming, the annual average temperature of the Weihe river basin increases with fluctuations from 1966 to 2017 at a rate of 0.35°C/10a. The temperature suddenly changed in 1997 and gradually increased from the northwest to the southeast. The temperatures across the basin will gradually rise in the future, but at a slower rate.
In the past 52 years, the other extreme precipitation indices have reduced slightly except for CDD and SDII with slight increases, in the Weihe river basin. Except for CDD that tends to decrease before and after the sudden change in temperature, the other indices decrease before and increase after the sudden change in temperature.
The extreme precipitation indices of the Weihe river basin have different spatial distributions. The spatial distributions of the CWD, R25, Rx5day, and SDII are similar to that of the temperature, namely they gradually increase from the northwest to the southeast, whereas the CDD gradually decreases from the northwest to the southeast. By analyzing these trends, extreme precipitation indices are found to differ in areas with high and low temperatures in the basin.
The P 95d–T relationship in the Weihe river basin shows the hook structure. The response intensity of extreme precipitation to climatic warming is dominated by super-C–C and C–C scaling, showing nonuniform spatial distribution characteristics. The peak temperature gradually increases from the west to the east and ranges from 11 to 20°C.
We would like to express our sincere thanks for help from the China Meteorolgical Administration.
Funding information: This research was supported by the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2019491411).
Author contributions: WBH wrote the manuscript. QQ conducted supervision. HXF conducted investigation and method research. DJX collated the data. The authors applied the SDC approach for the sequence of authors.
Conflict of interest: The authors state no conflict of interest.
 Zarekarizi M, Rana A, Moradkhani H. Precipitation extremes and their relation to climatic indices in the pacific Northwest USA. Clim Dyn. 2018;50(11):4519–37.10.1007/s00382-017-3888-2Search in Google Scholar
 She DX, Xia J, Zhang YY, Du H. Temporal and spatial variation and statistical characteristics of extreme precipitation in Huaihe River Basin in recent 50 years. J Geogr. 2011;66(9):1200–10 (in chinese).Search in Google Scholar
 Ren JQ, Guo CM, Liu YX. Temporal and spatial variation characteristics of extreme temperature index in Jilin Province from 1961 to 2015. J Ecol. 2017;11:232–42 (in chinese).Search in Google Scholar
 Ma MY, Han YP, Wang QM. Temporal and spatial variation law of extreme precipitation in Haihe River Basin and its relationship with atmospheric circulation. Hydropower Energy Sci. 2019;37(6):1–4, 74 (in chinese).Search in Google Scholar
 Ren GY, Feng GL, Yan ZW. Review and prospect of extreme climate change observation and research in China. Clim Environ Res. 2010;15(4):337–53 (in chinese).Search in Google Scholar
 Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, et al. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. 2006;111(D5):D05109.10.1029/2005JD006290Search in Google Scholar
 Ye H, Fetzer EJ, Wong S, Behrangi A, Yang D, Lambrigtson BH. Increasing atmospheric water vapor and higher daily precipitation intensity over Northern Eurasia. Geophys Res Lett. 2015;42(21):9404–10.10.1002/2015GL066104Search in Google Scholar
 Yin JB, Guo S, Gentine P, Sullivan SC, Gu L, He S, et al. Does the hook structure constrain future flood intensification under anthropogenic climate warming? Water Resour Res. 2021;57:e2020WR028491.10.1029/2020WR028491Search in Google Scholar
 Huang SZ, Huang Q, Chang JX, Zhu YL, Leng GY, Xing L. Drought structure based on a nonparametric multivariate standardized drought index across the Yellow river basin, China. J Hydrol. 2015;530:127–36.10.1016/j.jhydrol.2015.09.042Search in Google Scholar
 Wang XJ, Jiang RG, Xie JC. Variation trend and catastrophe analysis of runoff in the main stream of Weihe River. J Water Resour Water Transport Eng. 2019;2:33–40 (in chinese).Search in Google Scholar
 Huang XF, Wu BH, Cui P. Analysis on driving force of drought and flood evolution in Weihe River Basin in 550A. People’s Yellow River. 2020;42(S2):1–4 + 8 (in chinese).Search in Google Scholar
 Hu HN, Liu YG, Ma K. Centennial scale flood and drought characteristics and their influencing factors in the Weihe River Basin. Bull Soil Water Conserv. 2020;40(2):255–62 (in chinese).Search in Google Scholar
 Zhang J, Zhang K, Wang S. Analysis of temporal and spatial variation of extreme precipitation in the source area of three rivers in Shaanxi, Gansu and Ningxia in recent 50 years. J Hehai Univ (Nat Sci Ed). 2021;49(3):288–94.Search in Google Scholar
 Ma WD, Liu FG, Zhou Q, Chen Q, Liu F, Chen YP. Analysis of extreme precipitation characteristics on the Qinghai Tibet Plateau from 1961 to 2017. J Nat Resour. 2020;35(12):3039–50 (in chinese).10.31497/zrzyxb.20201218Search in Google Scholar
 Yang XX, Wu J, Liu J. Temporal and spatial variation characteristics of extreme precipitation and impact analysis of ENSO events in Poyang Lake Basin. Water Resour Res. 2021;10(1):12 (in chinese).10.3390/atmos12111522Search in Google Scholar
 Li SL, Gong ZW, Shi ZB. Analysis on variation characteristics of extreme precipitation index in Shandong Province in recent 50 years. Res Soil water Conserv. 2016;23(4):120–7 (in chinese).Search in Google Scholar
 Zhou SY, Su Z, Li Q. Change trend analysis of five major extreme precipitation indexes in Guangxi. Meteorolog Res Appl. 2012;33(3):8–13 (in chinese).Search in Google Scholar
 Zhang X, Yang F. RClimDex (1.0) user manual. Canada: Climate Research Branch Environment; 2004. p. 22Search in Google Scholar
 Zhang Y, Sui CG, Zhang RJ. Analysis on the change trend of China’s marine environmental quality based on Mann Kendall method . Environ Pollut Prev. 2019;41(2):76–80 (in chinese).Search in Google Scholar
 Zhang SW, Cong ZT, Ni GH. Comparative analysis of trend test methods based on China’s meteorological data. Prog Water Sci. 2013;24(4):490–6 (in chinese).Search in Google Scholar
 Wang XX, Zuo XQ, Yang ZN. Temporal and spatial variation of precipitation in Chengdu from 1980 to 2016 based on Mann Kendall test and information entropy. China Soil Water Conserv Sci. 2019;17(4):26–33 (in chinese).Search in Google Scholar
 Dogan M, Ulke A, Cigizoglu HK. Trend direction changes of Turkish temperature series in the first half of 1990s. Theor Appl Climatol. 2015;121(1–2):23–39.10.1007/s00704-014-1209-9Search in Google Scholar
 Chen GC, Xie P. Hydrological variability analysis based on heuristic segmentation algorithm. J Sun Yat Sen Univ (Nat Sci Ed). 2008;47(5):122–5 (in chinese).Search in Google Scholar
 Huang SZ, Huang Q, Wang YM. Variation diagnosis of runoff series based on heuristic segmentation and approximate entropy method. J Sun Yat Sen Univ (Nat Sci Ed). 2014;53(4):154–60 (in chinese).Search in Google Scholar
 Huang DQ, Yan PW, Liu GP, Zhu Jian. Study on the relationship between extreme precipitation and temperature in warm season – Taking Anhui Province as an example. Clim Environ Res. 2017;22(5):623–32 (in chinese).Search in Google Scholar
 Wang JW, Liao WL, Wang DG, Chen XH. Correlation between extreme precipitation and temperature in Chinese mainland. J Zhongshan Univ (Nat Sci). 2017;56(6):22–30 (in chinese).Search in Google Scholar
 Hardwick Jones R, Westrs S, Sharma A. Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity. Geophys Res Lett. 2010;37(22):n/a.10.1029/2010GL045081Search in Google Scholar
 Du LL, Zhou XY, Zhao YP, Wang SG. Climatic characteristics of extreme precipitation index and its relationship with precipitation and temperature in Handan, Hebei. Drought Meteorol. 2017;35(6):968–974 + 1003 (in chinese).Search in Google Scholar
 Gao HY, Cai XL, He H. Impact of urbanization on temperature change trend in Xi’an. J Geogr. 2009;64(9):1093–102 (in chinese).Search in Google Scholar
 Yin JB, Gentine P, Zhou S, Sullivan CS, Wang R, Guo SL. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat Commun. 2018;9:4389.10.1038/s41467-018-06765-2Search in Google Scholar PubMed PubMed Central
 Kharin VV, Zwiers FW, Zhang X, Hegerl GC. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J Clim. 2007;20:1419–44.10.1175/JCLI4066.1Search in Google Scholar
 Chen X, Quan Q, Zhang K, Wei J. Spatiotemporal characteristics and attribution of dry/wet conditions in the Weihe River Basin within a typical monsoon transition zone of East Asia over the recent 547 years. Environ Model Softw. 2021;9:105116.10.1016/j.envsoft.2021.105116Search in Google Scholar
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