Rating Performances of Global Climate Models in Capturing Monsoon Rainfall Patterns in Sri Lanka

Climate change plays a significant role in decision making regarding water management. Understanding and projecting the future climate of Sri Lanka is crucial for the development of appropriate adaptation and mitigation strategies. This research paper focuses on evaluating the performance of the Multiple Imputation by Chained Equations (MICE) imputation package for filling in missing data and the latest Coupled Model Intercomparison Project phase 6 (CMIP6) models for simulating the monsoon climate in Sri Lanka. The research utilizes observed daily precipitation data from 27 meteorological stations and employs the predictive mean matching (PMM) and normal imputation (Norm) methods for MICE algorithm to impute missing data. The performance of 15 CMIP6 models is evaluated using statistical techniques. The evaluation methodology includes assessing the models based on distance from the average solution using the Evaluation Based on Distance from Average Solution (EDAS) method. The results and analysis of the study provide insights into the performance of the CMIP6 models in simulating the monsoon climate of Sri Lanka in different climatic zones. The findings contribute to the selection of a suitable climate model for future climate change studies in Sri Lanka and support the development of effective adaptation and mitigation strategies in response to a changing climate.