@article{JU2025, 
author = {Qin JU and Xiaozan MA and Yanli LIU and Feng YAN and Cuishan LIU and Shiyang WANG and Zhuoxi LYU and Naifeng ZHANG},
title = {Evaluation for CMIP6 global climate models in simulating precipitation and temperature over the Huaihe River Basin based on rank score method},
year = {2025},
journal = {Journal of Hohai University (Natural Sciences)},
volume = {53},
number = {6},
pages = {12-20},
keywords = {CMIP6 climate model, precipitation, temperature, simulation performance, sensitivity analysis, the Huaihe River Basin},
url = {https://www.sciopen.com/article/10.3876/j.issn.1000-1980.2025.06.002},
doi = {10.3876/j.issn.1000-1980.2025.06.002},
abstract = {By using simulation data of precipitation and temperature in the Huaihe River Basin from 1985 to 2014 from 22 global climate models (GCMs) of the coupled model intercomparison project in phase 6 (CMIP6), the simulation performance of GCMs was comprehensively assessed by means of a rank score method with seven indexes selected, and the sensitivity of the scores to each index was analyzed. The spatial simulation performance of the selected optimal GCMs was evaluated. The results reveal significant differences in the simulation performance of monthly average precipitation and temperature in the Huaihe River Basin among the models. Overall, the GCMs perform better in simulating temperatures, but there is a general overestimation of monthly average precipitation. The top five models with the best comprehensive rank score are EC-Earth3 (7.83), EC-Earth3-Veg (7.66), ACCESS-CM2 (7.62), TaiESM1 (7.27), and FGOALS-f3-L (7.20). The rank score results for precipitation are most sensitive to the standard deviation, the statistics of Mann-Kendall trend analysis (z), and the slope of Mann-Kendall (β), while those for temperature show high sensitivity to z and β. Different combinations of rank score indexes moderately influence the scores. The EC-Earth3 model, identified as the most optimal, accurately reproduces the spatial distribution of precipitation but performs slightly less effectively for the spatial distribution of temperature.}
}