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Extreme precipitation events are frequent under global warming, leading to severe social, economic, and environmental damages. Therefore, a coupled stepwise clustered copula downscaling (SCCD) method is developed to explore the spatial and temporal variability of extreme precipitation in Eastern China under two shared socioeconomic pathways (SSPs). The performance of SCCD in reproducing historical climatology is assessed by comparing its simulated values with the observed data. The result demonstrates that SCCD performs well in modeling climate variables. In addition, the seasonal variations, probability distributions, and absolute changes of nine extreme precipitation indices in the future periods (i.e., 2024—2060 and 2061—2100) are compared with their performance in the historical period (i.e., 1981—2020). The results show that the precipitation in Eastern China shows an increasing trend over time. For example, compared to the historical period, the mean annual precipitation increases by 9.4% and 13.6% for the periods 20424–2060 and 2060–2100 under SSP585, respectively. The spatial variability and age trends suggest that future precipitation extremes also show significant regional differences. By the end of the twenty-first century, both frequencies and intensities of extreme precipitation at high latitudes have changed significantly. Under both SSP scenarios, all nine indices (except Consecutive Dry Days) increase by more than 10% in the future period at the high latitudes, especially for Very heavy precipitation days (R20), where the increase is more than 50% in most areas. In addition, the impacts of different emission scenarios on precipitation in autumn and winter are significantly greater than those in spring and summer. The results can provide a scientific basis for decision-makers to develop mitigation and adaptation policies to minimize the risks caused by climate change.

This study analyses future changes in temperature and precipitation over the Yangtze River Basin (YRB) throughout the twenty-first century by developing a regional factorial cluster analysis (RFCA) model on the basis of bias correction and spatial disaggregation (BCSD), cluster analysis and multilevel factorial analysis (MFA). In detail, BCSD is presented to downscale climate variables (i.e., daily mean temperature, maximum temperature, and total precipitation) of multiple global climate models (GCMs) under four shared socioeconomic pathways (SSPs). Evaluations shows BCSD can reasonably reproduce high spatial resolution climate predictions, especially temperature variables with great spatial correlation coefficients (i.e., larger than 0.9). Future climate changes are quantified by the Standard Euclidean Distance (SED), indicating climate change over YRB would spatially and seasonally undergo uneven distribution. Temperature changes have an overall south-to-north trend, with high variations in winter and summer. Precipitation changes over the upper reaches in winter and summer have larger variations than lower ones. Two hotspots are clustered with SED stabilities greater than 1.8, distributed in the Tibetan Plateau and the Yunnan–Guizhou Plateau, respectively. The effects of uncertainties (i.e., periods, GCMs and SSPs) and their interactions on predictions are analyzed through MFA. The individual effects of SSPs factor and its interactions with periods are worthy of consideration. The largest variation is observed under SSP585, with a total SED of 1.050; the smallest variation is observed under SSP370, with a total SED of 1.034. The interaction of SSPs and periods leads to a relatively stable interdecadal total SED under SSP126, which remains around 1.040.

In this study, the fifth generation ECMWF reanalysis (ERA5) reanalysis datasets and three global climate models (GCMs) were selected as the inputs of the stepwise-clustered heat stress downscaling (SCHSD) method to simulate the future heat stress indices in East China. The heat stress indices included the Heat Index ( HI ), Humidex ( HUMIDEX ) and the simplified Wet Bulb Globe Temperature ( sWBGT ). Three GCMs (i.e., CanESM5, INM-CM4-8 and MPI-ESM1-2-HR) under two Shared Socioeconomic Pathway (SSP245 and SSP585) were input into the SCHSD model to develop downscaled climate projections. To verify the SCHSD model, the reproduction results from three GCMs during the period of 1990–2014 were compared to daily observational data (i.e., HI , HUMIDEX and sWBGT ). The verification results suggest that the coefficient of determination ( R 2 ) of the stations in the northern part of East China mostly exceeds 0.8, while the R 2 of the stations in the southern part of East China is mostly in the range of 0.6 to 0.8. The projection results suggest that the future heat stress in East China would generally maintain an upward trend from 2021 to 2100. The largest change in heat stress is projected in June under the period of 2021–2100. The results also show that the highest average of days in the danger category is 78.9 in summer under the SSP585 scenario, accounting for 86% of the total summer (June, July and August). Highlights A stepwise-clustered heat stress downscaling (SCHSD) approach was developed to analyze the future changes in heat stress changes in East China. The coefficient of determination ( R 2 ) of the stations in the northern part of East China mostly exceeds 0.8, while the R 2 of the stations in the southern part of East China is mostly in the range of 0.8 to 0.6. The highest average of days in the danger category is 78.9 in summer under the SSP585 scenario, accounting for 86% of the total summer (June, July and August).

The study is focused on the ecological-fragile and water-shortage region in Northwest China (NWC). A drought-heatwave cluster projection (DHCP) approach is developed based on Stepwise Cluster Analysis (SCA) and multi-level factor analysis (MFA). A multi-model ensemble consisting of 5 global climate models (GCMs) under two shared socioeconomic pathways (i.e., SSPs) is used in the approach. It could investigate the spatiotemporal characteristics of heatwaves, drought, and compound drought-heatwave events (CDHEs) in NWC. A precise projection is given by SCA in 48 stations and four indicators of CDHEs (i.e., CEN, CEDU, CEI, and CEM) are thus calculated. Finally, the individual and interactive effects of uncertainties (i.e., period, SSP, and GCM) are analyzed by MFA. Results show the SCA method can effectively reproduce precise projections for temperature and precipitation for NWC. The frequency, intensity, and duration of heatwaves increase significantly under SSP5-8.5. Drought will ease and then intensify projected by all SSPs. The long-term intensification trend of drought is more pronounced under SSP5-8.5. CDHEs will increase, especially under SSP5-8.5 by the 2090s. These parameters will increase to 4.59 events, 2.86 days per event, 16.7°C per event, and 4.69 days, respectively. Higher increases are found in southeastern Qinghai, northwestern Gansu, and northern Xinjiang. Period affects CDHEs projection mostly. The GCM selection and its interaction with period also affect the projection significantly. DHCP provides a comprehensive analysis of heatwaves and droughts. It is equally applicable to other water shortage areas and is expected to help better manage water resources.

This paper deals with the fuzzy information processing method in the research of the relationship between two variables or among multi-variables, with the undetermined or fuzzy features, different from that in the traditional statistical method. The reliability and effectiveness of the method have been tested and confirmed in the numerical simulation for a set of man-made data of precursory anomalous parameters and earthquake elements. Finally the relation between the actual monthly frequency of small earthquakes occurring in Huoshan, Anhui Province, China and the magnitude of future stronger earthquakes, as two variables, has been analyzed by the method. It seems to the authors that more reasonable and perfect results could be given with a quantitative analysis of accession degree in fuzzy mathematics by using this method than that of traditional statistical correlation analysis.[PUBLICATION ABSTRACT]

在山东黄河三角洲自然保护区,大鸨属于冬候鸟,越冬时间为每年11月至次年3月,2015年在保护区内观测记录到32只大鸨,主要栖息在农田,以冬小麦苗、玉米、大豆、稻粒、植物种子为食。目前大鸨越冬地面积萎缩和破坏严重,大鸨栖息地质量正在下降,应通过扩大大鸨栖息地面积,改善栖息地环境质量,在保护区内试行湿地生态补偿措施,开展日常巡护监测及加强宣传教育等措施以改善大鸨越冬环境和越冬数量。