A combined storyline-statistical approach for conditional extreme event attribution

Abstract

Quantifying the influence of anthropogenic global warming on extreme events requires both physical and statistical understanding. We present a framework combining two complementary conditional attribution methods: spectrally nudged storylines and flow-analogues. Applied to the 2018 Central European heatwave, storylines project an area-mean intensification of 1.7 °C per degree of global warming. Despite no detected changes in terms of atmospheric blocking by the flow-analogue approach, the combined framework further indicates that heatwaves under similar atmospheric conditions, and exceeding the storyline-projected intensities, may become more frequent and extreme at their corresponding warming levels than the factual 2018 event was under present conditions. Our study shows that the 2018 heatwave, with an intensity of 2.2 °C and a return period of 1-in-277 years today, becomes a 6.6 °C event with a 1-in-26 year probability in a +4 K world in the absence of other dynamical trends. This behavior reveals the importance of other physical mechanisms and interactions beyond the atmospheric circulation pattern and thermodynamic conditions influencing the occurrence and intensification of heatwaves. We conclude that this combined framework is promising for climate change attribution of individual extreme events, offering both a physical assessment of anthropogenic warming and its associated likelihood while accounting for potential shifts in atmospheric dynamics.