Probable Maximum Precipitation Estimation Under Climate Change: Addressing Sampling and Bias Correction Uncertainties Using Large Ensemble Climate Simulations

Abstract

Abstract This study presents an integrated framework for estimating Probable Maximum Precipitation (PMP)‐equivalent return levels under climate change, addressing two critical challenges: sampling uncertainty for rare events and the validation of bias correction under different global warming scenarios. Leveraging large regional climate model ensembles, the framework combines spatial transposition, based on L‐moment homogeneity, with ensemble pooling to expand the effective sample size to nearly 10,000 independent annual maxima per location. Results show that spatial transposition significantly reduces uncertainty in rare‐event return levels, narrowing 95% confidence intervals by over 60% for the 10,000‐year event. Improvements were most notable in the upper tail, where empirical quantiles closely matched Generalized Extreme Value fits due to increased parameter stability from the expanded sample size. Subsequently, three bias correction techniques, CDF‐transform (CDFt), Quantile Delta Mapping (QDM), and Quantile Mapping (QM), were applied to CanRCM4‐LE and evaluated against CRCM5‐LE using a pseudo‐observation framework that allowed assessment under different warming scenarios. CDFt consistently provided the best agreement with distributional properties and empirical quantiles, followed by QDM. Using the best‐performing bias correction method applied to CRCM5‐LE with ERA5 reanalysis serving as the observational reference, PMP‐equivalent return levels at grid cells corresponding to 2,730 dam locations across Northeastern North America, show spatially coherent increases, with median 100,000‐year return levels rising by ∼57% under +1.5°C and ∼77% under +3.0°C warming. Broader spatial patterns mirror this intensification, underscoring the thermodynamic drivers of future extremes. The framework enables robust, reproducible PMP estimation and supports adaptive design strategies for critical infrastructure under climate uncertainty.