Assessment of Extreme Precipitation Indices over Indochina and South China in CMIP6 Models

Precipitation extremes over the Indochina and South China (INCSC) region simulated by 40 global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) were quantitatively assessed based on the skill score metrics of four extreme precipitation indices when compared with observational results from a high-resolution daily precipitation dataset for 1958-2014. The results show that it is difficult for most of the CMIP6 models to reproduce the observed spatial pattern of extreme precipitation indices in the INCSC region. The interannual variability of the extreme precipitation indices is relatively better simulated for South China than for Indochina. In general, most of the CMIP6 models perform better in South China compared with Indochina when taking both the simulations of spatial pattern and interannual variability into consideration. Only three models (EC-EARTHS, EC-EARTH3-Veg, and NorESM2-MM) can successfully reproduce both the spatial pattern and the interannual variability for the INCSC region. Through model ranking, the multimodel ensemble generated by a selection of the most skillful models leads to a more realistic simulation of the extreme precipitation indices both in South China and Indochina. Better simulation of the meridional wind component over South China and the water vapor convergence over Indochina can partly reduce the wet biases, resulting in a more realistic simulation of extreme precipitation indices over the INCSC region.

Automated summary

Most CMIP6 models struggle to accurately reproduce the observed spatial pattern of extreme precipitation indices in the Indochina and South China region. However, the interannual variability of extreme precipitation indices is relatively better simulated for South China than for Indochina. Only three models can successfully replicate both the spatial pattern and the interannual variability for the INCSC region. A multimodel ensemble generated by selecting the most skillful models provides a more realistic simulation of extreme precipitation indices in both South China and Indochina, suggesting that improving the simulation of specific atmospheric components can help reduce biases in extreme precipitation simulations.