Evaluation of NEX-GDDP-CMIP6 in simulation performance and drought capture utility over China – based on DISO

Global climate models (GCMs) are state-of-the-art tools for understanding climate change and predicting the future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 were evaluated by the ‘DISO’ (Distance between Indices of Simulation and Observation), a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the correlation coefficient (CC), false alarm rate (FAR) and probability of detection (POD). Then, it was combined with the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evaporation Index (SPEI) to evaluate the drought detection capability of NEX-GDDP-CMIP6. The result shows that (1) NEX-GDDP-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trends but fails to capture precipitation's fluctuation characteristics; (2) The best-performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246); (3) The multi-mode ensembles (16MME) perform better than the best single model, indicating that a multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifies drought well in arid, while the SPI is recommended for other climate classifications in China.