Evaluation and future projection of compound extreme events in China using CMIP6 models

In the context of global warming, the frequency and intensity of extreme temperature and precipitation events are increasing. Under this scenario, an increase in compound extreme events would pose a greater risk to human society and ecosystem. However, the modelling and future projection of various types of compound events remain a great challenge. Therefore, in this study, we first evaluate the simulation performance of CMIP6 climate models for six types of compound extreme event in China in terms of spatial distribution, interannual variability and interdependence during the historical period. Based on this performance evaluation, we select the 6 best models, which are then utilized to project future changes of compound events under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the next three time periods (2015-2043, 2044-2071, and 2072-2100). The results are as follows: (1) The performance of GCMs in the simulation of extreme temperature indices is better than that for extreme precipitation indices, and positive biases exist in extreme precipitation indices for most models. (2) In the historical period, the consistency between the simulation and observation of compound extreme events show large heterogeneity for different models and different events. (3) The frequency percentage and spatial extent of compound extreme events associated with warm extremes will increase in the future, while compound extreme events associated with cold extremes, except for SP&Tn10, will show a decreasing trend. (4) Spatially, the Pr90&Tx90, Hot&Dry and Cold&Dry in the southern region will increase significantly. In the Tibetan Plateau, except for Pr90&Tn10, all other compound extreme events will have a large increase. In Northeast and North China, SP&Tx90 and Pr90&Tx90 are projected to increase. The results from this study underline how the evaluation and the future projection of compound extreme events have the potential to improve the understanding of climate model uncertainty and future risk of climate extremes, and provide a scientific basis for relevant agencies to formulate appropriate adaptation policies.