This study evaluates the performance of the Coupled Model Intercomparison Project Phases 6 models (CMIP6) in simulating the seasonal evolution and extreme precipitation indices in the western North Pacific and East Asia region (WNP-EA), and compare the results with those from CMIP Phases 5 (CMIP5). In the ensemble of CMIP6 models, the seasonal evolution simulation demonstrates improvements in seasonal northward migration of the rain band from spring to summer and more intense precipitation, resulting in a higher skill score in the CMIP6 ensemble than the CMIP5 one. In general, the skill scores for the spatial pattern of simple daily intensity (SDII), total rainfall occurrence (Totfq), and consecutive dry days (CDD) are higher than those of extreme precipitation intensity (R99p). The CMIP6 ensemble mostly gains higher skill scores for extreme precipitation indices in the wet season. However, the improvement is limited in extreme indices during spring and fall. The probability distributions for maximum 1-day precipitation (RX1day), maximum 5-day precipitation (RX5day), and R99p in the CMIP6 models demonstrate a more realistic shape and stronger intensity, indicating the improvement over the CMIP5 models. However, the biased distributions of the overestimated (underestimated) occurrence for lighter (heavier) SDII and shorter (longer) duration CDD cases remain as a problem in CMIP6 model simulations. The higher skill scores for the spatial pattern of SDII, Totfq, and CDD are likely due to the compensation between the biased distributions mentioned above, implying further improvements are needed for correcting the deficiency in simulating the precipitation occurrence.

这项研究评估了耦合模型比较项目第6阶段模型（CMIP6）在模拟北太平洋西部和东亚地区（WNP-EA）的季节演变和极端降水指数方面的性能，并将结果与​​CMIP阶段的结果进行了比较5（CMIP5）。在CMIP6模型的集合中，季节演变模拟表明，春季到夏季雨带的北向季节性迁移得到改善，并且降水更加强烈，因此CMIP6集合中的技能得分高于CMIP5。通常，简单日强度（SDII），总降雨发生（Totfq）和连续干旱日（CDD）的空间模式的技能得分高于极端降水强度（R99p）的技能得分。CMIP6系集在雨季的极端降水指数方面通常获得较高的技能得分。但是，在春季和秋季，极限指数的改善受到限制。CMIP6模型中最大1天降水量（RX1day），最大5天降水量（RX5day）和R99p的概率分布显示出更逼真的形状和更强的强度，表明相对于CMIP5模型有所改善。但是，对于较轻（较重）的SDII和较短（较长）持续时间的CDD情况，高估（低估）事件的偏向分布仍然是CMIP6模型仿真中的问题。SDII，Totfq和CDD的空间模式的较高技能得分可能是由于上述偏差分布之间的补偿所致，