Islam et al. (2026) Probable Maximum Precipitation Estimation Under Climate Change: Addressing Sampling and Bias Correction Uncertainties Using Large Ensemble Climate Simulations

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Identification

• Journal: Water Resources Research
• Year: 2026
• Date: 2026-10-01
• Authors: Md. Robiul Islam, Mohammad Reza Najafi, Alex J. Cannon
• DOI: 10.1029/2026wr043507

Research Groups

• University of Toronto Department of Civil Engineering
• McGill University School of Environment
• Canadian Centre for Climate Modelling and Analysis (CCCma)

Short Summary

This study presents an integrated framework to estimate Probable Maximum Precipitation (PMP)‐equivalent return levels under climate change, addressing sampling uncertainty and bias correction challenges. The framework combines spatial transposition with ensemble pooling to improve rare event return level estimates.

Objective

• Estimate PMP-equivalent return levels under different global warming scenarios, accounting for sampling uncertainty and bias correction

Study Configuration

• Spatial Scale: Regional scale over Northeastern North America
• Temporal Scale: Annual maxima (100-year to 10,000-year return periods)

Methodology and Data

• Models used: CanRCM4-LE and CRCM5-LE regional climate models
• Data sources: ERA5 reanalysis data for observational reference

Main Results

• Spatial transposition significantly reduces uncertainty in rare-event return levels (95% confidence intervals narrowed by over 60% for the 10,000-year event)
• CDF-transform bias correction method provides best agreement with distributional properties and empirical quantiles
• PMP-equivalent return levels show spatially coherent increases under +1.5°C and +3.0°C warming scenarios (median 100,000-year return levels rising by ∼57% and ∼77%, respectively)

Contributions

• Original framework combining spatial transposition with ensemble pooling to improve rare event return level estimates
• Robust PMP estimation method enabling adaptive design strategies for critical infrastructure under climate uncertainty

Funding

• Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants RGPIN-2019-06753 and RGPIN-2016-05556
• Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) Grant 2018-CFCA-01

Citation

@article{Islam2026Probable,
  author = {Islam, Md. Robiul and Najafi, Mohammad Reza and Cannon, Alex J.},
  title = {Probable Maximum Precipitation Estimation Under Climate Change: Addressing Sampling and Bias Correction Uncertainties Using Large Ensemble Climate Simulations},
  journal = {Water Resources Research},
  year = {2026},
  doi = {10.1029/2026wr043507},
  url = {https://doi.org/10.1029/2026wr043507}
}