Assessment of CMIP6 global climate models in reconstructing rainfall climatology of Bangladesh

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979-2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision-analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre-monsoon and a decreasing trend in post-monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI-ESM1-2-LR, MPI-ESM1-2-HR, and GFDL-ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization.

Automated summary

This study evaluated the performance of 15 Global Climate Models in replicating the rainfall climatology, variability, and trends in Bangladesh. Most models underestimated annual rainfall but accurately reproduced the spatial features of rainfall. The ensemble mean of the models showed higher skill in reconstructing rainfall characteristics, with MPI-ESM1-2-LR, MPI-ESM1-2-HR, and GFDL-ESM4 identified as the most effective models.