Targeted weather regimes identify circulation patterns behind Western European summer heat extremes and trends

Abstract Western European heat extremes have intensified in recent decades, with their rate of warming outpacing the global mean. Against this general human-induced warming trend, understanding the circulation patterns that drive such heat extremes is crucial. Weather-regime approaches have been widely used to characterise large-scale circulation variability; however, conventional classifications are not optimised to identify the dynamical drivers of extremes. Here we apply a novel targeted machine learning-based approach, the regression mixture-model variational autoencoder (RMM-VAE), to characterise summer weather regimes relevant for Western European temperatures. We compare its performance against the standard, non-targeted k -means approach and find RMM-VAE to yield dynamically coherent regimes while being more informative of (extreme) temperatures in the target region. Our analysis identifies a southerly-flow regime that accounts for the vast majority of heatwave days, while k -means disperses them across multiple regimes. Moreover, the seasonal frequency of this impact-relevant regime, combined with global mean temperature, explains a large fraction of interannual variability in both mean ( R <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.84</mml:mn> </mml:mrow> </mml:math> ) and extreme summer temperatures (90th percentile; R <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mi/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>0.65</mml:mn> </mml:mrow> </mml:math> ), with predictive skill persisting out-of-sample tests. Finally, this simple regression model allows us to attribute 34% of total summer warming in Western Europe and about 70% of the observed ‘excess’ warming (relative to the global mean) to an observed increase in the identified southerly flow circulation pattern, which we quantify to be largely forced. Our results demonstrate that targeted weather regime approaches can sharpen the link between circulation and surface extremes, offering attribution of regional warming. Furthermore, the identified regimes provide interpretable predictors with potential for improving seasonal forecasts and climate risk assessments.