Improvement in the skill of CMIP6 decadal hindcasts for extreme rainfall events over the Indian summer monsoon region

Abstract

Decadal climate predictions have been widely used to predict the near-term climate information relevant for decision-making at multi-year timescales. In the present study, we evaluate the quality of the Coupled Model Intercomparison Project phase-6 (CMIP6) Decadal Climate Prediction Project (DCPP) hindcasts in capturing the extreme rainfall events (EREs) over the monsoon core region during Indian summer monsoon season (June–September) up to lead years 1–10. For the first time, in this study, we have used quantile mapping approach to downscale and bias correct the DCPP CMIP6 simulation/hindcast rainfall for the better representation of EREs. Detailed analysis suggests that the models in general strongly underestimate the rainfall variability over the summer monsoon region. However, after the downscaling and bias correction, the representation of rainfall variability and intensity improved multifold. The bias-corrected decadal hindcasts in fact show ~ 80% improvement in capturing the frequency, intensity, and spatial distribution of rainfall associated with the EREs. Present study brought out a downscaled DCPP product, with potential prediction skill for EREs over India. It is important to highlight that the models predict an increase in the small and medium-area EREs as compared to the large-area EREs over the monsoon core region for the decade 2019–2028.