This study evaluates the performance of the CMIP6 models in simulating monsoon rainfall and moisture tracking in the Yangtze River basin. The findings reveal varying degrees of accuracy across different regions of the basin during the monsoon period. Downstream and midstream regions demonstrate higher accuracy, whereas upstream areas exhibit lower precision, along with an overall trend of overestimation. The evaluation encompasses the timing of monsoon months, as well as the peak month, while analyzing the simulation’s accuracy for rainfall. It also entails an overarching examination through a Taylor Diagram and Taylor skill scores, which spotlight models with superior and inferior performance. EC-Earth3 exhibits commendable performance, whereas models like IITM-ESM showcase poorer results. Furthermore, moisture tracking assessments, utilizing the WAM2layers model, identify limitations within the CMIP6 model in terms of replicating water vapor sources and pathways, especially in proximity to geographical features such as the Himalayas and the coastline. In addition to the basin itself, the CMIP6 model simulates central Asia as the main source of evaporation, rather than the Indian Ocean, according to the results of
ERA5. However, no obvious pattern differences are shown between the different CMIP6 GCMs. Persistent challenges stem from data availability and numerical inconsistencies, necessitating enhancements in both the CMIP6 models and the WAM2layers code.

Since the mid-19th century global temperatures have increased significantly, so does the intensity of extreme rainfall. Previous studies have suggested that by the end of the century extreme rainfall intensity would increase by 10.5% and 28.2% for the low and high emission scenarios, and the centennial extreme rainfall would increase by 13.5% and 38.3%. For this model-based result, our aim is to verify it with observed historical data and find out the growth rate of extreme rainfalls. In this study, the MEV method was used to analyze the changes in the intensity of daily extreme rainfall in the 1-year, 10-year, and 100-year return periods at 29 stations and linearly fit them to determine the trends, and finally found that: the extreme rainfall showed an increasing trend in 70% of the stations in all data years, and the average growth rates in the 1-year, 10-year, and 100-year return period are 9.73%, 8.06% and 12.00%, re- spectively. From 1950 to the present, 56% of the stations have an increasing trend of extreme rainfall and the growth rate is 6% and 4% for 1-year and 10-year events, 2.4% for the 100-year events. For the growth rate per decade, the 1-year and 10-year events grow faster after 1950 than data from 1850, and the growth rate of the 100-year events becomes smaller.