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arXiv · 2509.05043

Detecting extreme event-driven causality

Siyang Yu·Yu Huang·Zuntao Fu
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Abstract

The occurrence of some extreme events (such as marine heatwaves or exceptional circulations) can cause other extreme events (such as heatwave, drought and flood). These concurrent extreme events have a great impact on environment and human health. However, how to detect and quantify the causes and impacts of these extreme events by a data-driven way is still unsolved. In this study, the dynamic system method is extended to develop a method for detecting the causality between extreme events. Taking the coupled Lorenz-Lorenz systems with extreme event-driven coupling as an example, it is demonstrated that this proposed detecting method is able to capture the extreme event-driven causality, with even better causality detecting performance between concurrent extreme events. Comparison among three kinds of measured series, full measurements outperform partial ones in event-to-event causality detecting. The successful applicability of our proposed approach in Walker circulation phenomenon indicates that our method contributes a novel way to the study of causal inference in complex systems. This method offers valuable insights into multi-scale, nonlinear dynamics, particularly in uncovering associations among extreme events.

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BibTeXRIS

Siyang Yu, Yu Huang, Zuntao Fu. 2025-09-05. Detecting extreme event-driven causality. https://doi.org/10.1016/j.chaos.2025.117138

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