Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in numerical weather prediction and artificial intelligence tools, extreme weather still present challenges. More specifically, identifying the precursors of such extreme weather events and how these precursors may evolve under climate change remain unclear. In this paper, we propose to use post-hoc interpretability methods to construct relevance weather maps that show the key extreme-weather precursors identified by deep learning models. We then compare this machine view with existing domain knowledge to understand whether deep learning models identified patterns in data that may enrich our understanding of extreme-weather precursors. We finally bin these relevant maps into different multi-year time periods to understand the role that climate change is having on these precursors. The experiments are carried out on Indochina heatwaves, but the methodology can be readily extended to other extreme weather events worldwide.

本研究针对气候变化下极端天气事件前兆识别不清的问题，提出了一种后验可解释性方法，通过构建相关天气图，展示深度学习模型识别的关键极端天气前兆。研究表明，这些模型能够揭示有助于理解极端天气前兆的模式，且该方法可扩展至全球其他极端天气事件的研究。

XAI4Extremes：一个用于理解气候变化下极端天气前兆的可解释机器学习框架