Assessing Dominant Uncertainties in Future Precipitation Projections for a Hurricane‐Prone Region

Abstract

Accurate projection of future precipitation remains challenging due to uncertainties in reference data sets, bias correction and global climate models (GCMs). Here, we evaluated these uncertainties across 13 major cities of the U.S. Gulf Coast, a hurricane‐prone region, under 192 historical and future scenarios. Four reference gridded precipitation data sets, eight CMIP6 GCMs and one HighResMIP model (CMCC‐CM2‐VHR4) were first evaluated against in situ measurements. All GCMs were then bias corrected using four reference data sets and two statistical techniques, empirical quantile mapping (EQM) and a hybrid EQM with linear correction (EQM‐LIN). The bias corrected outputs were then evaluated against in situ measurements. All gridded data sets, HighResMIP and GCMs tended to overestimate light events but underestimate extremes. PRISM and CPC showed the strongest and weakest agreement, respectively, while AORC outperformed others across the Southwest Florida Peninsula, where land‐sea interactions and spatial heterogeneity challenge coarse‐resolution models. Bias correction substantially improved model performance up to the 90th percentile, reducing MAE and RMSE by more than 70% in some cases. However, the performance degraded beyond this percentile; very high percentiles (≥95th) remained underestimated. Future projections under SSP2‐4.5 and SSP5‐8.5 indicated that bias correction reduces inter‐model spread of extreme precipitation indices (Rx1day, SDII and R95p) by approximately 60%–80%, while HighResMIP projections generally remained within the CMIP6 ensemble range. These findings highlighted that credible projections of future precipitation depend more on the representativeness and quality of reference data sets and bias correction technique than the GCMs. The results provide guidance for improving future precipitation projections, updating intensity‐duration‐frequency curves and advancing resilience planning in hurricane‐prone regions.