Detecting extreme event-driven causality

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.