The CASCADE project has four major research branches centered on low-likelihood high-impact (LLHIs) events. Our efforts are motivated by conclusions from the sixth Intergovernmental Panel on Climate Change Report, which states that we currently have low confidence in current and future projections of LLHIs. Here, I present work on LLHI heatwave events in the observational record and discuss how our results inform dynamical modeling efforts across the CASCADE research portfolio.

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of LLHI heatwave events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures may become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds can 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 that account for the spatial coherence of individual heatwave events. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Equipped with robust estimates of LLHI heatwave thresholds, probabilities, and uncertainties from the historical period, our results provide an important baseline for studying corresponding events in large dynamical and machine learning ensembles.