Stochastic Simulation of Unprecedented Rainfall Events under Climate Change: From Hurricane Harvey to Continental-Scale Risk Assessment

Unprecedented rainfall events are characterized by extremely high magnitudes and very low probabilities. Such events are occurring more frequently under a warming climate, despite being poorly represented in historical records. While former studies investgated physical drivers of such extremes, statistical approaches to quantify their likelihood and impacts remain limited. The current study presents a serial-type stochastic rainfall generator (SRG) explicitly designed to simulate unprecedented rainfall by incorporating non-stationarity through resampling and perturbation of model parameters governing the power-law tails of rainfall distributions. The approach is first evaluated over Southeast Texas using daily rainfall simulations for the 2017 Hurricane Harvey event, based on rainfall accumulation data from eight weather stations. By adjusting two power-law tuning parameters to represent warming conditions, the SRG successfully generates Harvey-like rainfall extremes. Simulated rainfall magnitudes associated with 50-, 100-, 250-, and 500-year return periods substantially exceed historical estimates. Additionally, the inferred return period of Harvey-scale rainfall closely aligns with previous independent assessments. The framework is subsequently extended to the Indian region, where thirty-six climate-change-relevant precipitation scenarios are generated by perturbing SRG parameters. High-performance computing is used to simulate daily rainfall across the domain, from which rainfall return levels and depth–duration–frequency (DDF) curves are derived. Results indicate substantial increases in rainfall return levels across all frequencies when unprecedented events are considered, particularly in coastal, northeastern, and Himalayan regions. Consistent spatial patterns and low spatial uncertainty across climate zones demonstrate the robustness of the SRG despite its point-based formulation. The proposed framework provides a statistically grounded pathway for revising design storms and supporting climate-resilient flood risk management under non-stationary climate conditions.