Kaiser et al. (2026) Assessing Dominant Uncertainties in Future Precipitation Projections for a Hurricane‐Prone Region

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Earth s Future
• Year: 2026
• Date: 2026-07-01
• Authors: Ebrahim Ahmadisharaf, Ceyda Polatel, Gabriele Villarini, Vasubandhu Misra, Tirusew Asefa, Amir AghaKouchak
• DOI: 10.1029/2025ef007680

Research Groups

• University of Miami Rosenstiel School of Marine and Atmospheric Science
• National Oceanic and Atmospheric Administration (NOAA)
• Cooperative Institute for Marine and Atmospheric Studies (CIMAS)

Short Summary

This study evaluates the uncertainties in reference data sets, bias correction, and global climate models (GCMs) for projecting future precipitation across 13 major cities of the U.S. Gulf Coast under various historical and future scenarios.

Objective

• Investigate the impact of reference data set quality and bias correction technique on GCM performance in simulating precipitation extremes

Study Configuration

• Spatial Scale: Regional (U.S. Gulf Coast, with a focus on 13 major cities)
• Temporal Scale: Historical (192 historical scenarios) and future (under SSP2-4.5 and SSP5-8.5)

Methodology and Data

• Models used: CMIP6 GCMs (eight models), HighResMIP model (CMCC-CM2-VHR4)
• Data sources: In situ measurements, four reference gridded precipitation data sets (PRISM, CPC, AORC, and others)

Main Results

• Bias correction significantly improved model performance up to the 90th percentile, reducing MAE and RMSE by more than 70% in some cases.
• However, performance degraded beyond this percentile, with very high percentiles (≥95th) remaining 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%.

Contributions

• This study highlights the importance of reference data set quality and bias correction technique in improving GCM performance for simulating precipitation extremes.
• The results provide guidance for updating intensity-duration-frequency curves and advancing resilience planning in hurricane-prone regions.

Funding

• National Oceanic and Atmospheric Administration (NOAA) - Grant Number: [Not specified]
• Cooperative Institute for Marine and Atmospheric Studies (CIMAS) - Grant Number: [Not specified]

Citation

@article{Kaiser2026Assessing,
  author = {Kaiser, Samiul and Ahmadisharaf, Ebrahim and Polatel, Ceyda and Villarini, Gabriele and Misra, Vasubandhu and Asefa, Tirusew and AghaKouchak, Amir},
  title = {Assessing Dominant Uncertainties in Future Precipitation Projections for a Hurricane‐Prone Region},
  journal = {Earth s Future},
  year = {2026},
  doi = {10.1029/2025ef007680},
  url = {https://doi.org/10.1029/2025ef007680}
}