Evaluating CMIP6 model performance of wet and dry spells by using novel climate rainfall indices over the Southeast Asia Region

Understanding and accurately predicting precipitation events is crucial due to their significant impacts on human lives and economies, especially in the Southeast Asia (SEA) region. This study evaluates the performance of CMIP6 models in simulating precipitation indices, focusing on both spatial patterns and temporal variations from 1981 to 2010. Models were classified into two groups: the 'Good_group,' which demonstrated robust performance, and the 'Bad_group,' which exhibited notable biases. The Good_group comprising ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, MIROC6, NorESM2-LM, and NorESM2-MM consistently performed better in simulating total precipitation (PRCPTOT), consecutive dry days (CDD), and daily intensity (SDII). These models showed smaller biases and more accurate representations of spatial patterns and temporal variability in the SEA region. Conversely, the Bad_group including EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MPI-ESM1-2-LR exhibited significant biases and poorer performance. Specifically, Good_group models provided a more realistic simulation of PRCPTOT and SDII with reduced biases, whereas Bad_group models showed larger errors, especially in northeastern India and Myanmar. For CDD, Good_group models estimated fewer consecutive dry days than observed, while Bad_group models overestimated them. Despite advancements in CMIP6 models, including higher resolutions and improved parameterizations, challenges persist in accurately simulating wet spells dynamics in the complex SEA region. This study identifies the most skilful models and areas for improvement, offering valuable insights for model selection and enhancing climate projections and adaptation strategies in the SEA region.