Quantifying Very Extreme Precipitation and Temperature Using Huge Ensembles Generated by Machine Learning-based Climate Model Emulators

Weather extremes produce major impacts on society and ecosystems and are likely to change in likelihood and magnitude with climate change. However, very low probability events are hard to characterize statistically using observations or climate model output because of short records/runs. For precipitation, consideration of such events arises in quantifying Probable Maximum Precipitation (PMP), namely estimating extreme precipitation magnitudes for designing and assessing critical infrastructure. A recent National Academies report on modernizing PMP estimation proposed using huge climate model-based ensembles to estimate extreme quantiles, possibly through machine learning-based ensemble boosting. Here we assess such an approach for the contiguous United States using a huge ensemble (10560 years) from a state-of-the-art emulator (ACE2) trained on ERA5 reanalysis. The results indicate that one can practically estimate very extreme precipitation and temperature quantiles using appropriate statistical extreme value techniques. More specifically, the results provide evidence for (1) the use of threshold-exceedance methods with a sufficiently high threshold for reliable estimation (necessary for precipitation), (2) the robustness of results to variations in extremes by season and storm type, and (3) well-constrained statistical uncertainty. Our results also show that the emulator produces extremes outside the range of the ERA5 training data. While this suggests that such emulators have potential for quantifying the climatology of extremes, we do not extensively investigate if this particular emulator is fit for purpose. Our focus is on how to use huge ensembles to estimate very extreme statistics, and we expect the results to be relevant for future improved emulators.