Near-term prediction of surface temperature extremes over India in the CMIP6-DCPP models

Decadal climate prediction project (DCPP) hindcasts/predictions from Coupled Model Intercomparison Project phase-6 (CMIP6) provide vital information on the near-future climate up to a decade. Coarse-resolution climate models however fail to accurately represent the regional climatic features thereby limiting the prediction skill. In this study, DCPP models maximum and minimum surface temperatures (T max and T min ) hindcasts are downscaled and bias-corrected (DBC) over the Indian region to examine the characteristics of heat/cold waves for 1–10 lead years. It is found that the DBC T max (T min ) captures the heat (cold) waves intensity, frequency, and spatial distribution over India quite effectively. After DBC, representation of extreme thresholds of T max (T min ) over India has improved by 82(90)%. Further, the areal extent of extremely high (low) temperatures associated with the large and small area heat (cold) waves are well characterized after DBC up to 10-year lead. Importantly, DBC product showed superior skills in capturing the regional temperature peaks associated with large and small area heatwave/cold wave days and is limited before DBC. After DBC, the temperature extremes (both warm and cold) display enhanced intensity with increased (decreased) mean T max (T min ) by ~ 0.6 °C (0.3 °C) in the recent decade (2017–2026) compared to the previous decade (2007–2016) during April to June (November to February). The present analysis of DCPP models opens the door for the near future or decadal prediction of temperature extremes by demonstrating the increased prediction ability across India for T max and T min following DBC. The study has important implications for decision-making and may help different stakeholders, policymakers, and disaster managers.