Evaluation of events of extreme temperature change between neighboring days in CMIP6 models over China

In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluation studies of ETCN events with global climate models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 35 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. The results showed that from 1981 to 2013, the annual average EW frequencies showed an increasing trend over China, but a decreasing trend for EC events, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events occurred in autumn. Additionally, the performances of the CMIP6 models were quite different between EC and EW events. The models could capture the annual cycle of EC and EW events well, and the simulations of EW events were generally more reliable than those of EC events. Furthermore, most CMIP6 models overestimated the frequency of EW events but underestimated the frequency of EC events in China. The CMIP6 models could capture the trends in EC events in China but fail to simulate them in EW events. The interannual variability of EW events exhibited relatively better performance than that of EC events. The CMIP6 MME effectively improved the capabilities of the models to simulate the climatology of ETCN events. Individual CMIP6 models exhibited better performances than the CMIP6 MME in terms of the trend and interannual variability. Finally, according to the overall ranking of the CMIP6 models, MPI-ESM-1–2-HAM and FGOALS-f3-L achieved the best performance in simulating EW and EC events, respectively. This study selected the optimal models in different regions at the seasonal and annual scales, providing theoretical support for the frequency projection and modeling improvement of ETCN events.