A stepwise-clustered copula downscaling approach for ensemble analyses of discrete and interactive features in precipitation-extreme variations: a case study for eastern China

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.