Developing a Framework for Large Hail Prediction Utilizing Monthly Occurrence Probability and Storm Environments

Climate change is expected to influence the frequency and intensity of severe convective storms , with implications for large hail (LH) and associated economic losses. This study develops an empirical model to project monthly LH days across the conterminous United States by linking NOAA Severe Weather Reports with severe storm environments derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. LH days are estimated from Large-Hail-Environment (LHE) days, identified using Most Unstable Convective Available Potential Energy (MUCAPE) and a conservative physically based threshold. This model performance is evaluated against the convection-permitting CONUS404 reanalysis and independent NOAA hail reports. Results show strong correlations between LHE and LH days at annual and monthly scales, especially in hail-prone regions. Projections under SSP2-4.5 and SSP5-8.5 scenarios, derived from a four-model CMIP6 ensemble, indicate an overall increase in LH days through the late 21st century, with marked regional and seasonal variations. Multi-model agreement exceeds 75% across the eastern United States, confirming the robustness of projected increases. The high-emission scenario produces larger magnitude changes and broader geographic expansion compared to SSP2-4.5, demonstrating sensitivity to the emission pathway. The study highlights the value of simple, observation-anchored empirical models for translating modeled storm environments into practical hail risk metrics. Findings have implications for adaptation planning, insurance, and agricultural risk management.