AI 中文总结
研究概率性极端事件归因中证据综合方法，评估现有方法不足，提出针对性修改及新的参数级综合方法，该方法能跨多阈值和条件推断，模拟研究显示改进明显，案例研究体现其实用性。
AI 中文摘要
概率性极端事件归因旨在量化人为气候变化如何改变一类极端事件的可能性或强度。现有研究通常通过先为每个数据源估计归因度量（如概率比或强度变化），然后综合所得估计值，来结合观测产品和气候模型集合的证据。我们批判性地评估了这种方法，识别了文献中相应基准程序的潜在缺点，并提出了有针对性的修改和一种新的参数级综合方法。后者结合了潜在非平稳分布回归参数的估计值，从而能够跨多个事件阈值和反事实气候条件进行推断。在受控模拟研究中，所提出的修改大大改进了基准程序，而参数级综合提供了具有竞争力的整体性能。通过对2024年9月与鲍里斯风暴相关的强降水的案例研究说明了其实用性。
英文摘要
Probabilistic extreme event attribution aims to quantify how anthropogenic climate change has altered the likelihood or intensity of a class of extreme events. Existing studies commonly combine evidence from observational products and climate-model ensembles by first estimating attribution measures, such as probability ratios or intensity changes, for each data source and then synthesizing the resulting estimates. We critically assess this approach, identify potential shortcomings of a respective benchmark procedure from the literature, and propose both targeted modifications and a new parameter-level synthesis method. The latter combines estimates of the underlying nonstationary distributional regression parameters, thereby enabling inference across multiple event thresholds and counterfactual climate conditions. In controlled simulation studies, the proposed modifications substantially improve upon the benchmark procedure, while parameter-level synthesis provides competitive overall performance. The practical usefulness is illustrated through a case study of the heavy precipitation associated with Storm Boris in September 2024.
