Distributional autoencoders trained on climate model simulations model full conditional distributions of European temperature fields to enable probabilistic storyline attribution, illustrated by higher intensities and probability ratios for a 2003-like heatwave in 2028 and 2053.
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A non-stationary Markov process with bivariate extreme value theory attributes full heatwave time series over Europe to anthropogenic forcing via likelihood ratios between ERA5 and CMIP6 runs, finding strong evidence since the 1970s but no signal beyond mean temperature increase.
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
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Non-stationary time series attribution for heatwaves over Europe
A non-stationary Markov process with bivariate extreme value theory attributes full heatwave time series over Europe to anthropogenic forcing via likelihood ratios between ERA5 and CMIP6 runs, finding strong evidence since the 1970s but no signal beyond mean temperature increase.