Evidence Synthesis in Probabilistic Extreme Event Attribution: From Attribution Measures to Model Parameters

2026-07-21

AI Summary

This study addresses a key limitation in current extreme event attribution practices, which typically estimate attribution metrics separately from observations and models before combining them—a procedure prone to introducing bias. To overcome this, the authors propose a novel parameter-level evidence synthesis framework that directly integrates regression parameters from nonstationary distribution models, thereby transcending the conventional reliance on post-hoc aggregation of attribution metrics. The approach enables unified inference across multiple thresholds and climate scenarios, including counterfactual conditions, by jointly incorporating probability ratio and intensity change metrics. Simulation experiments demonstrate that the method significantly outperforms standard attribution workflows. The framework is successfully applied to attribute the extreme precipitation associated with Storm Boris in September 2024.

Abstract

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.

Problem

• extreme event attribution
• evidence synthesis
• probabilistic attribution
• climate change
• model parameters

Innovation

• parameter-level synthesis
• probabilistic event attribution
• nonstationary distributional regression
• evidence synthesis
• extreme event attribution