Analysis of Extreme Precipitation under the New Low Radiative Forcing Scenario in the Inland River Basin of Northwest China


 With global warming, extreme weather events in various regions have become more abnormal. To study the variation characteristics of extreme precipitation in the inland river basin (IRB) of Northwest China, this paper selected four global climate models (GCMs) under a low radiative forcing scenario to analyze eight-core extreme precipitation indices in the basin. According to the changes of each index in the near future (NF) and the far future (FF), the changes of extreme precipitation have been determined under a low radiative forcing scenario in the basin. Our study shows that all four models can capture seasonality and all have uncertainty. The PRCPTOT showed a trend of decreasing from the center to the southeast and northwest. The SDII showed opposite trends in mountainous and central desert regions. The number of CDD will decrease, while the CWD will change significantly. The P1025 and PG25 will increase by more than 50%. The Rx1day and Rx5day are mainly distributed in some areas such as the desert and Gobi. Although we have predicted the future trend of extreme precipitation in the IRB, the precise prediction of extreme precipitation remains to be further studied.


Introduction
Since the Industrial Revolution, the massive emission of carbon dioxide and other greenhouse gases has gradually warmed the global climate. The frequency of global extreme precipitation events has increased signi cantly in the context of global warming (Berg et al., 2013;Donat et al., 2016;Easterling et al., 2016).
Compared with the long-term continuous increase in temperature, changes in extreme precipitation events are more likely to cause serious casualties and property losses (Zhang and Zhou, 2020). Therefore, accurate simulation and prediction of extreme precipitation are particularly important (Bai et  directly support the writing of the Intergovernmental Panel on Climate Change (IPCC) assessment report (Zhou et al., 2019). The simulation data of the Coupled Model Intercomparison Phase 6 (CMIP6) has been submitted so far. Therefore, the use of CMIP6 data for extreme precipitation analysis has become a new research focus.
The number of rainfall days has decreased in most regions of China since the 1960s (Liu et al., 2005), but the total annual rainfall has increased signi cantly (Zhu et al., 2011), and the corresponding extreme precipitation events have become more frequent (Wang et al., 2012). Some scholars have used highresolution regional models to estimate the future precipitation and extreme precipitation in China (Gao et  . The northwestern region is replenished by precipitation and mountain snowmelt water. It is an arid and semi-arid region, which is more sensitive to precipitation uctuations (Zhu et al., 2018 radiative forcing under the Shared Socioeconomic Pathways (SSPs1-1.9), and this kind of low forcing radiation scenario has not appeared in CMIP5 in the past. The climate models under SSPs1-1.9 have been developed to limit the temperature increase to 1.5°C to achieve the sustainability goals in the Paris Agreement (Eyring et al., 2016;Stouffer et al., 2017). However, there is still a lack of relevant research using this low radiative forcing scenario to analyze extreme precipitation in Northwest China.
In this study, we use four CMIP6 GCMs under the Shared Socioeconomic Pathways 1-1.9. The aim of the paper is twofold. Firstly, to correct the four CMIP6 GCMs based on the CN05.1 grid observation data set obtained by more than 2400 national-level stations of the National Meteorological Information Center in China. Secondly, to explore the changes of extreme precipitation indices of the NF (2021-2050) and the FF (2071-2100) in the IRB of Northwest China and their impact on precipitation in the basin. The study results could provide useful information for the formulation of sustainable water resource utilization policies and the harmonious and stable development of society in Northwest China.

Study area
Based on the principles of the Chinese Academy of Sciences Resource and Environment Data Center for the division of national water resources, this paper divides the country into nine major river basins according to river basins (

Data
Five models of CANESM5 (CE), GFDL-ESM4 (GE), MRI-ESM2 (ME), MIROC6 (MI), and IPSL-CM6A-LR (IL) have provided the simulation results of SSPs1-1.9. The atmospheric resolution of CE is 500km, while the other four models are 100km and 250km (Gupta et al., 2020). Therefore, four models have been selected for this study, namely GE, ME, MI, and IL. Basic information on the models is given in Table 1. Further details can be obtained online at https://www.wcrp-climate.org/wgcm-cmip. Precipitation data applied for evaluation is obtained from a daily 0.25°×0.25° meteorological dataset during 1961-2018 (CN05.1). The dataset is obtained by superimposing the climate eld and the anomaly eld after interpolating respectively from the observation data of more than 2400 stations in China by using the anomaly approximation method (Wu and Gao, 2013).
To ensure the accuracy and consistency of the data, we adopt 1°×1° spatial resolution and select the precipitation data from 1961 to 2014 as the baseline period, 2021-2050 as the NF, and 2071-2100 as the FF.

Bias correction
In this study, we evaluate the four models by the percentage of deviation and the root mean square error (RMSE). Climate model output data suffer from various types of inherent biases. Therefore, it becomes essential to correct these biases before impact assessment analysis (Cannon, where X MF and X MF − C are respectively the future simulation value and deviation correction value of the model, F MF is the CDF in the future, F − 1 OH is the inverse function of the CDF of historical observation data, while F − 1 MH is the inverse function of the CDF of historical data in the model.

Precipitation extreme indices
To explore the changing nature of climate change in the IRB of Northwest China simulated in CMIP6 under SSP1-1.9, we used eight-core extreme precipitation indices. They are PRCPTOT, SDII, CDD, CWD, P1025, PG25, Rx1day, and Rx5day. The SDII is the PRCPTOT divided by the number of days with precipitation greater than or equal to 1 mm. Table 2 shows the characteristics of the extreme precipitation indices used.

Seasonality variability in CMIP6 historical data
The four seasons are distinct in the IRB of northwest China, the climate is dry, and the distribution of  500mm in spring ( Fig. 2f-2j), 550mm in summer ( Fig. 2k-2o), and 220mm in autumn ( Fig. 2p-2t). The IRB has complex landform types, including snow-capped mountains, oases, and deserts, which makes precipitation in seasons show huge spatial differences. Comparing the basic data of the selected CMIP6 climate models, an almost similar spatial distribution of precipitation was observed in all climate models. In spring, the simulated precipitation of each model shows the decreasing trend from northwest and southwest to northeast and southeast, while the precipitation shows a decreasing trend from southwest to northeast in summer.
Comparing the precipitation distribution maps of each season horizontally, the precipitation data of the IL model is the closest to the historical observations. The IL model can well represent the extremely high value of precipitation in the precipitation data of spring and summer, while the GE model can capture that in winter and autumn. The MI model overestimates the precipitation in the IRB, especially in the southwestern region of each season. The ME model's estimation of precipitation in this area is not stable enough. The winter is generally low, and the other three seasons are low in some places and high in some places.   Fig. 5a, while the central desert region has the least precipitation. The PRCPTOT in Fig.  5b and annual average precipitation in Fig. 5a shows similar distributions. From Fig. 5b and Fig. 5c, it can be seen that the PRCPTOT and the SDII have a strong correlation, especially in the southeast and northwest regions, while the correlation is poor in the central desert area and the Kunlun Mountain area.

Model evaluation and bias correction
The maximum observed CDD appears in the Taklimakan Desert area, and the minimum appears in the northwestern region (Fig. 5d). The CWD value of the IRB in the northwest is generally small, and the CWD value is large only in the eastern part of Qilian, near the Tianshan Mountains and the Qinghai-Tibet Plateau in the south (Fig. 5e). The number of moderate rain days with daily precipitation of 10-25mm only occurs in the Tianshan Mountains, the southern part of Gangdise Mountains, the eastern part of Qilian Mountains, and the Daxingan Mountains in the northeast (Fig. 5f). Heavy rain with a daily rainfall of more than 25mm only occurs in the Daxinganling Mountains in the northeast, and the annual average value is 1.5 days (Fig. 5g). The distribution of Rx1day and Rx5day in the IRB is highly consistent, showing a trend of decreasing from the southeast and northwest to the middle (Fig. 5h-5i). The maximum value of Rx1day is about 35mm, and the maximum value of Rx5day is about 60mm.  (Figs. 6a-6b and 6e-6f). The PRCPTOT index of the Taklimakan Desert in the FF is 6 times that of the observed data, while that of the NF is only 2 times. In the MI and IL models (Figs. 6c-6d and 6g-6h), both times (i.e., NF and FF) have shown a similar spatial distribution of the PRCPTOT, that is, the annual precipitation has increased signi cantly in the desert area.

Analysis of extreme precipitation in the future
In the GE and ME models ( Fig. 6i and 6j), the percentage change of SDII in the NF shows that the surrounding areas such as Tianshan, Kunlun, Qilian, and Daxinganling Mountains are around -10%, while the central desert area is around +10%, and the Gangdise Mountain is around +30%. The percentage change of the SDII in the FF of the Tianshan, Kunlun, Qilian, and Taklimakan Desert areas will decrease compared to the NF, while the Gangdise Mountains in the southeast will increase (Fig. 6m and 6n). In the MI and IL models ( Fig. 6k and 6l), the percentage change of the SDII in the NF is similar to the other two models, except that the values of some grids in desert areas are more negative. However, some grid values in the central desert area will decrease in the FF and will decrease in the south and east, especially in the Gangdise Mountains and Qilian Mountains (Fig. 6o and 6p). In the GE and ME models (Figs. 7a-7b and 7e-7j), it can be seen that some grids in the southeast and northeast regions show positive changes in different periods, such as Qilian Mountains, Gangdisi Mountains, and Daxinganling Mountains, while most of the grids in the desert, northwest and southwest regions, such as the Kunlun Mountains and the Altai Mountains show negative changes. In the MI and IL models (Figs. 7c-7d and 7g-7h), the distribution of the CDD change percentage in the NF is similar to the other two models, but there will be subtle changes in the FF, which are mainly re ected in the Tianshan Mountains and the Altai Mountains. The change rate of some grids in the Altai Mountains and Tianshan Mountains has increased by more than 80%. In general, the CDD change percentage of most grids in different periods and different models corresponds to -60% to +20%.
In the GE and ME models ( Fig. 7i and 7j), the CWD change percentage of most grids will exceed 40%, and only some grids will show negative values in the NF, such as Tianshan, Kunlun Mountains, and Qilian Mountains. However, most of the grids in the FF are above 80%, except for the Qinghai-Tibet Plateau from the Kunlun Mountains to Gangdis Mountains, Qilian Mountains, and Tianshan Mountains ( Fig. 7m and   7n). In the MI and IL models ( Fig. 7k and 7l), the distribution is quite different from the other two models in the NF, and most of the grids exceed 80%. Only a small part of the grids are negative, mainly distributed in Tianshan, Kunlun, and Qilian Mountains. The distribution of the percentage change of the CWD is nearly half of the negative value in the FF, distributed in mountainous areas, such as the Altai Mountains, Tianshan Mountains, and the Kunlun Mountains, as well as desert areas ( Fig. 7o and 7p). In general, the CWD change percentage of most grids in different periods and different models corresponds to -10% to +100%. Compared with the NF (Fig. 8e), the spatial distribution of the percentage change has a strong consistency in the FF, but the positive value is not obvious in the desert area. In the ME model ( Fig. 8b and   8f), both NF and FF show a spatial distribution similar to the GE model in P1025. However, the percentage changes of the grids in the central desert area and the Gangdise Mountains are slightly different from those of the GE model in the NF. In the MI and IL models (Figs. 8c-8d and 8g-8h), the percentage change of the P1025 of most grids in the NF and the FF corresponds to 0 to 10%, some grids in desert areas exceed 20%, and the grid values of Tianshan, Kunlun, and Gangdese mountains correspond to -5%.
In the GE and ME models (Figs. 8i-8j and 8m-

Discussion And Conclusions
In this study, we have used the eight extreme precipitation indices of four CMIP6 GCMs to analyze the extreme precipitation in the NF and the FF in the IRB when the global warming level is lower than 1.5°C. The study found that the four models can capture the seasonality, especially the IL model, whose The CMIP6 GCMs behave differently in the simulation of annual precipitation in different regions, and it has a certain correlation with altitude. The precipitation in the Taklimakan Desert, the Gurbantungut Desert, and the Gobi area from Hami to Yinshan increased by 2-6 times, while the precipitation in the mountainous areas did not change much. At the end of July 2021, the Yuqi area located in the Taklimakan Desert was hit by oods, covering an area of more than 300 square kilometers, which con rmed the simulation results. In the future, some areas in the deserts, Gobi, and mountains will have more large ows and short-lasting precipitation, which will be more prone to ood disasters.
The number of accumulated dry days in the northwest IRB will decrease, which means that precipitation will become more frequent and last longer in most areas. The future number of moderate rain days will generally increase in the entire IRB, while the increase in heavy rain days will be mainly concentrated in the mountains, deserts, and Gobi areas. With the increase of people's awareness of environmental protection and the control of global warming, the number of heavy rain days in the FF will show a decreasing trend, especially in the desert and Gobi areas. In addition, moderate to heavy rains have increased by more than 50% in the desert, Gobi, and Gangdese mountains of the Qinghai-Tibet Plateau, which may make these areas more abundant in vegetation.
Similarly, Rx1day and Rx5day re ect similar distributions to other indices. The areas with the largest growth are mainly distributed in the desert and Gobi areas, and some are distributed in the Gangdise Mountains and Yinshan-Daxinganling area.
Even when the global temperature rises by 1.5°C, climate change still has a huge impact on precipitation in the northwestern IRB. Although we predict the future trend of extreme precipitation in the IRB, the precise prediction of extreme precipitation still needs further research. This is not only important for disaster prevention and control but also important for the formulation of sustainable water resources utilization policies in Northwest China and the harmonious and stable development of society.  China's nine river basins and the geographical location of the study area  Calculate the percentage change of P1025 and P25 of all CMIP6 GCMs concerning the observed precipitation in the NF and the FF