A combined storyline-statistical approach for conditional extreme event attribution
Dalena León‐FonFay, Alexander Lemburg, Andreas H. Fink, Joaquim Ginete Pinto, Frauke Feser · Weather and Climate Dynamics · 2026
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 atmospheric blocking, the flow-analogue approach further indicates that heatwaves exceeding the storyline-projected intensities become far less rare at their corresponding warming levels than the factual 2018 event was under present conditions. Specifically, 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. 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.