Evaluation of the CMIP6 Precipitation Simulations Over Global Land.

Precipitation's temporal and spatial patterns under climate change significantly impact global terrestrial ecology and human social activities. Climate models are essential tools for assessing the impacts of climate change and formulating policies to address climate change. The evaluation results of historical climate model simulations can represent the reliability of their future simulations. This study evaluated the simulation capabilities of 41 historical All‐Forcing monthly precipitation simulations and three integrated models over global land in the Coupled Model Comparison Project Phase 6 (CMIP6). The results show that the simulation capability of global climate models (GCMs) in CMIP6 is highly variable overland around the world. This variability is manifested in two aspects: the spatial variability of the comprehensive simulation ability of each model in different geographical regions and climatic zones of the world and the significant difference in the simulation ability of different models in each region. These GCMs generally overestimate global monthly precipitation over land, with the exception of southeast Asia and tropical rainforest climate (Af), where all models underestimate monthly precipitation. Some GCMS can perform well regionally but poorly on the global scale. One example shows that EC‐Earth3's best capability at Cwc climatic zone, surpassing the integrated model, but failed to rank in the top 10 in 22 of the 29 climate zones. Our results highlight the need to select appropriate models for integration when conducting climate change studies at global and regional scales as a critical factor in studying climate change predictions. Plain Language Summary: This study evaluated 41 monthly precipitation models in Coupled Model Comparison Project Phase 6 from geographic regionalization and global climate classification at the global land scale. Quantile integration was used to get three integration models to participate in the evaluation. Indicators used include correlation coefficient, Taylor skill score, Wasserstein distance, and common indicators of deviation analysis. Conclusions mainly include the performance of integrated models is generally better than that of single models on the global scale. The variation deviations of most indices have similar patterns to the corresponding mean deviations. In different geographic regions and climate types around the world, global climate models that perform better are different. In addition, this research also gives the suitable models in each region of the world and the upper limit of the simulation capability of all models in each area of the world. Key Points: The comprehensive simulation capability of each global climate model (GCM) has spatial variability in different geographical regions and climate zonesThere is no single model can achieve good performance on a global scaleMultiple model ensembles (quantile integration in this study) can significantly improve GCM's applicability on the large scale [ABSTRACT FROM AUTHOR]