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.

翻译：天气极端事件对社会和生态系统产生重大影响，且其发生概率与强度可能随气候变化而改变。然而，由于观测记录或气候模式模拟时长的限制，极低概率事件的统计特征难以准确刻画。在降水研究中，此类事件的考量尤其体现在"可能最大降水"（PMP）的量化中，即估算用于关键基础设施设计与评估的极端降水强度。美国国家科学院近期一份关于PMP估算现代化的报告提出，可基于超大气候模式集合（或通过机器学习驱动的集合增强技术）来估算极端分位数。本研究使用基于ERA5再分析数据训练的最先进模拟器（ACE2）生成超大集合（10560年），针对美国本土评估了此类方法的可行性。结果表明，通过采用适当的统计极值分析技术，能够实际估算极端降水与温度的分位数。具体而言，研究结果证实：（1）使用具有足够高阈值的超阈值方法可实现可靠估算（这对降水估算尤为必要）；（2）结果对季节与风暴类型引起的极端事件变化具有稳健性；（3）统计不确定性可得到良好约束。我们的研究还显示，该模拟器能够生成超出ERA5训练数据范围的极端值。虽然这表明此类模拟器在量化极端气候特征方面具有潜力，但我们并未深入探究该特定模拟器是否完全适用于此目的。本研究重点在于如何利用超大集合估算极端统计量，预计相关结论对未来改进的模拟器具有参考价值。