Data-driven upper bounds and event attribution for unprecedented heatwaves

TL;DR

This paper investigates how data-driven methods can establish upper bounds for heatwave temperatures, revealing the influence of human activity on the likelihood of unprecedented heat events and improving future risk assessment.

Contribution

It introduces a novel approach combining extreme value theory with spatial statistics to better estimate upper bounds and attribute the increasing extremity of heatwaves to anthropogenic forcing.

Findings

Many unprecedented heatwaves are within data-driven upper bounds when using modern spatial methods.

There is a clear link between human influence and the likelihood of 'impossible' extreme temperatures.

Understanding heatwave thresholds aids in predicting and preparing for future record-breaking events.

Abstract

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed events that exceed a priori upper bound estimates, so-called "impossible" temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the "impossibility" of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.