A combined storyline-statistical approach for conditional attribution of climate extremes to global warming

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. The storyline approach provides insights on how a specific event is shaped by the thermodynamic conditions representing past (counterfactual), present (factual) and future global warming levels (+2K, +3K, +4K). The flow-analogue method provides a statistical analysis of the recurrence of the observed event, and the future storyline-projected events based on similar dynamical patterns that lead to the event of interest. Together, this combined approach allows us to determine not only the change in likelihood of an extreme event occurring as it did in the present, but also the probability that an intensified version (storyline-projected) of it occurred in the future.Applied to the 2018 Central European heatwave, storylines show an area-mean warming rate of 1.7 °C per degree of global warming. Through the flow-analogue method, it was evidenced that the atmospheric blocking leading to this event remains equally likely to occur regardless of global warming. Despite it, the storyline-projected intensities might become more frequent and extreme 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, is projected to intensify to 6.6 °C with a 1-in-26-years return period in a +4K world. This behavior revealed the importance of other physical mechanisms and interactions influencing the occurrence and intensification of heatwaves beyond the atmospheric circulation pattern and thermodynamic conditions. 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.