Formal extreme event attribution traditionally relies on large, computationally intensive climate-model ensembles, which often hampers the provision of information in real-time attribution. To overcome this, we propose a lightweight, interpretable framework that couples unsupervised anomaly detection with Bayesian deep learning enabling near-real-time attribution without statistical assumptions or costly climate model simulations. A convolutional variational autoencoder (VAE) is trained on daily 2 m temperature fields from the CMIP6 HadGEM3-GC31-LL Hist-NAT experiment (1850–2020) and early ERA5 reanalysis (1940–1980). The VAE achieves a spatial-mean correlation of 0. 9 over land and exhibits robust skill in discriminating warm days from non-extreme conditions across the full domain. Spatial gradients of the VAE’s reconstruction mean-squared error and Kullback–Leibler divergence yield three threshold-free metrics quantifying local departures from pre-industrial conditions. We examine their relationship with more statistical metrics like the counterfactual exceedance probability p₄ₗ, which measures the chance that an event drawn from the factual climate exceeds any event expected under the counterfactual scenario. Across the training period, these metrics exhibit a strong monotonic anti-correlation with p₄ₗ, demonstrating that VAE loss gradients encode meaningful dynamical–thermodynamic information. A Bayesian multi-layer perceptron calibrated solely on these metrics reproduces grid-point p₄ₗ with a mean spatial correlation of 0. 92, delivering exceedance probabilities alongside interquartile ranges. Case studies of four exceptional European heatwave summers (2010, 2018, 2019, and 2022) show that our framework accurately captures spatio-temporal patterns, assigns near-zero probabilities (p₄ₗ 0. 85 and the Maximal Information Coefficient (MIC) ≈ 1, indicating that they encode robust dynamical and thermodynamical information. Applied to four major European heatwaves (2010, 2018, 2019, 2022), the framework accurately reproduces spatio-temporal heatwave patterns, estimates near-zero probabilities for record-breaking events, and aligns with results from large ensembles, without relying on global-mean temperature predictors or parametric assumptions.
Zaninelli et al. (Tue,) studied this question.