December 2024
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The present study quantitatively assessed the thirteen (13) state-of-the-art statistically downscaled, bias-corrected, and high-resolution climate model datasets derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the extreme precipitation indices (rainfall, SDII, RX1DAY, RX5DAY, rainy days, and CDD) during the summer monsoon season (June through September) over India. A comparison between the model and observed data using skill score metrics such as Taylor’s skill score, interannual variability score (IVS), and Mielke’s (M) score has been carried out for the Indian region. Also used are the other performance metrics, such as bias and RMSE. Results show that the multi-model mean (MMM) captured the spatiotemporal variations of extreme precipitation indices to the maximum extent as the observed data show. This is evidenced by the pattern correlations of +0.96, +0.86, +0.93, +0.90, +0.82 and +0.75 for the mean rainfall, RX1Day, RX5Day, SDII, rainy days, and CDD, respectively between the MMM and observed data. The RMSE and bias plots showed that the CanESM5, followed by ACCESS-ESM2-5, could not perform better than the other models and the MMM in portraying extreme indices as IMD indicated. The M-score analysis revealed similar spatial patterns and intensity of the indices in MMM as obtained by the observed data. The lesser values of IVM (< 1.5) except for the CDD show that the models such as EC-Earth3, BCC-CSM2-MR, EC-Earth3-Veg, ACCESS-ESM1-5, INM-CM4-8 along with the MMM have more similarity in depicting the interannual variations as demonstrated by IMD for the different extreme precipitation indices.