How can AI tools be used to explore unprecedented future climate and weather extremes?

This study addresses recent calls for greater focus on understanding unprecedented extreme events (e.g. Kelder et al., 2025; Matthews et al., in review) by exploring the potential to use downscaled ‘synthetic data’ from climate model projections to train cutting-edge, computationally efficient deep learning models and generate very large ensembles of high-resolution extreme weather events under future perturbed climates. The study seeks to advance understanding of plausible upper limits in extreme high-impact, low-likelihood (HILL), record-shattering extremes and unprecedented tail risks, focusing initially on the threat of uncompensable heat with the potential to result in catastrophic mass mortality impacts. We address a number of open questions in this nascent field by testing a set of recently developed tools in new and innovative ways to understand the benefits and limitations of this approach. 

Can we generate new insights beyond what can be achieved using traditional methods, such as large ensembles of physics-based models and advances such as ensemble boosting? What are the benefits of producing very large stochastic ensembles of plausible extreme weather systems and how does this complement (or otherwise) other approaches with similar motivations (e.g. emulators)? Can we identify and validate plausible physical climate storylines leading to unprecedented extreme events e.g., by identifying and clustering meteorological setups leading to very large, compound, or concurrent non-contiguous regional extremes? Can we robustly constrain this method to ensure physical plausibility in unprecedented climates? Can we advance understanding of rare event probability under a non-stationary climate from various emissions pathways? What are the limitations due to aleatoric and epistemic uncertainty? How do we mitigate biases and limit their propagation? Can we investigate downward counterfactuals and identify meteorological conditions aligning with imagined worst-case scenarios?

By addressing these questions, this study seeks to advance knowledge of the threats posed by the most extreme plausible weather events posing potentially catastrophic risks to society.