Detection and attribution of heat waves with the Multivariate Autoencoder Flow-Analogue Method (MvAE-AM)

Heat waves (HWs) are complex, multivariate, extreme weather events that cause significant harm to human health, ecosystems, and economies. Correct detection and attribution of HWs to anthropogenic climate change is important to better understand the underlying mechanisms and to improve predictions. In this work, we address this issue and propose a multivariate version of a hybrid approach to reconstruct heat waves, consisting of the AM and deep Autoencoders (MvEA-AM algorithm), improving existing less effective methods used until now, such as the multivariate Analogue Method (MvAM). The proposed hybrid approach produces a more reliable representation of the event than the classical MvAM for reconstructing and attributing HWs in Europe. The explainable and interpretable analysis of the obtained results is based on leveraging the SHapley Additive exPlanations (SHAP) method to explain deep learning algorithms, a capability that is not achievable with the MvAM. This explainability analysis shows that our model learns useful features during the training of the algorithm, which are aligned with the Physics of the problem, and employs the correct features during reconstruction and attribution analysis of the HWs considered.