Evaluation of daily precipitation modeling performance from different CMIP6 datasets: A case study in the Hanjiang River basin

To effectively compare and analyze daily precipitation modeling capabilities across different CMIP6 datasets, our study introduces a novel method to compare daily precipitation models across CMIP6 datasets in the Hanjiang River Basin (HRB). We quantify indicators such as precipitation distribution, temporal correlation, wet-dry detection, extreme value error, and spatio-temporal variability, enabling a comprehensive rating of precipitation accuracy. It has been found that while both CMIP6(CP6) and NEX-GDDP-CMIP6 (GCP6) models show similar simulation accuracy, GCP6 excels in several aspects like distribution, temporal correlation, extreme value simulation, and spatial variability, yet lags in wet-dry detection and temporal change. Notably, using the comprehensive rating score (CRS) analysis, significant differences in precipitation simulation accuracy exist between models, particularly CP6, with variations of up to 0.22 (51.2%) between the highest and lowest scores. Among the top ten models, GCP6 occupies four positions such as MRI-ESM2-0, GFDL-CM4, MPI-ESM1-2-HR, and TaiESM1, while CP6 holds the remaining six like CanESM5, EC-Earth3-Veg, MPI-ESM1-2HR, GFDL-CM4, MRI-ESM2-0, and IPSL-CM6A-LR. These findings not only offer a clear understanding of the simulation performance of CMIP6 datasets across various precipitation characteristics, but also quantitatively compare the modeling capabilities of different models for watershed precipitation through CRS. This aids climate adaptation research, hydrological forecasting, and flood management in the basin. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.