Unprecedented rainfall events increase the magnitude of design storms

Abstract Climate change, driven by human activities and increasing greenhouse gas emissions, is pushing Earth’s climate toward a warmer state, as evidenced by long-term observations. The frequency and intensity of unprecedented rainfall events have increased in recent years, underscoring the urgent need to revise design storms and depth-duration frequency (DDF) curves to better adapt to and mitigate the impacts of climate change. This study used a serial type of stochastic rainfall generator (SRG) that is capable of simulating daily rainfall series by embedding unprecedented events to study extreme precipitation scenarios under the changing climate. By perturbing values of power law tuning parameters in the SRG model, we developed thirty-six precipitation scenarios, some of which directly correlate with the current climate change scenario, while others represent very extreme conditions. High-performance computing is employed to run the computationally intensive SRG for simulating thirty-six scenarios across the entire Indian region. These simulated scenarios were analyzed to prepare rainfall return level maps and DDF curves. The findings reveal substantial increases in rainfall return levels across all frequencies when unprecedented events are considered, with pronounced impacts in coastal, northeastern, and Himalayan regions. The spatial pattern of simulated extreme precipitation was consistent across all generated scenarios from SRG irrespective of the return periods. Minimal spatial uncertainty in return level estimates across climate zones is observed which confirms the robustness of the SRG model and spatial clusters of extreme rainfall are identified irrespective of SRG being a point model. The analysis in this study based on SRG simulated climate change scenarios offers crucial insights for revising design storms and for devising climate resilience and flood management strategies.