Research On The Future Risk Changes And Population Exposure Of China's Summer Compound Dry-hot Event

In the context of global warming,the number of summer compound hot-dry events in China has increased significantly.In this study,we used the day-by-day mean temperature and precipitation data of the CN05.1 gridded dataset for the time period1975-2014,and the BCC-CSM2-MR,MPI-ESM MR,and IPSL-CM5A-MR model data provided by CMIP6 to study the trends of compound hot-dry events in China during the historical summer period,focusing on the three composite dry heat event indicator characteristics and population exposure of the corresponding indicators,and the Copula model was used to analyze the probabilistic risk of compound hot-dry events.The three model data were revised with one-and multivariate deviations,and the optimal MBCn method was selected for the revision by comprehensive comparison to analyze the changes of compound hot-dry events in future periods,and to derive the changes of the risk of compound hot-dry events at 1.5 and 2°C.The degree of contribution of population and climate factors to the changes of exposure was further quantified.The main conclusions are as follows:(1)The correlation between summer temperature and precipitation in Chinese regions was examined,and then the spatial and temporal characteristics of the compound hot-dry events and its population exposure distribution characteristics were analyzed,and the risk changes of the compound hot-dry events in the historical period 1995-2014 relative to 1975-1994 were calculated based on the Copula model.The results show that the compound hot-dry events are highly prevalent in East China,Central China,Sichuan and Chongqing,and Northeast Inner Mongolia regions,and have a long duration and high intensity.Its risk changes showed an increasing trend in the historical period,and the regions with large increases were mainly located in the second and third terraces of Chinese topography,and the increase in the risk of compound hot-dry events was significantly larger in the northern regions than in the southern regions,while the increase in the eastern regions was significantly smaller than that in the western regions.The increase of its population exposure is concentrated in the eastern regions of China,and mainly in Beijing-Tianjin-Hebei,Yangtze River Delta and Pearl River Delta.(2)Based on the summer day-by-day mean temperature and precipitation data of three models,BCC-CSM2-MR,MPI-ESM-MR and IPSL-CM5A-MR,the correction effects of three bias correction methods,CDFT(cumulative probability distribution transformation)and MBCn and MBCr,were investigated.The results show that the multivariate bias correction method outperforms the CDFT in terms of simulation capability.The correlation coefficient and standard deviation ratio between the model and Observations are close to 1.The probability density curves of the simulated daily mean temperature and precipitation data are closer to the Observations after the correction,and the deviation of the model data from the compound hot-dry events in Xinjiang is also better corrected.(3)Based on the revised model data of MBCn,we explore the future change characteristics,risk changes and population exposure changes of the compound hot-dry events and the contribution of its influence factors.The results show that the frequency,intensity and duration of the compound hot-dry events in the future period show an increasing trend,with the most significant increase in risk in the eastern northeast and central northwest regions.The regional average risk of compound hot-dry events increases by 1.93 times and 2.34 times when the global warming is 1.5℃ and 2℃.The spatial pattern of the intensity population exposure and the number of days population exposure of the compound hot-dry events is basically consistent,and the duration population exposure of the compound hot-dry events is more prominent in Gansu and Qinghai.Climate factors occupy a larger proportion in the change of population exposure of compound hot-dry events-related indicators,and the influence of climate-demographic factors on population exposure is becoming more and more significant.