A Self-Supervised Analogue Framework for Probabilistic Subseasonal Forecasting of Heat Extremes

Accurate forecasting of climate extremes such as droughts, heatwaves, and heat stress episodes at subseasonal-to-seasonal (S2S) timescales is of high importance for the public health, energy, water management, and agriculture sectors. However, there is a communis opinio that these scales, commonly referred to as the "predictability desert", represents a major scientific challenge for accurate forecasting. Indeed, despite recent progress, both state-of-the-art numerical and deep learning-based weather forecasting models still exhibit limited skill in forecasting extreme events beyond ten days (Bodnar et al., 2025; Bi et al., 2023; Chen et al., 2023; Lam et al., 2023; Chattopadhyay et al., 2020).

In this work, an alternative approach is considered by revisiting analogue forecasting methods (Marina et al., 2026; Pérez-Aracil et al., 2024). In the spirit of the K-nearest neighbor algorithm, these methods are built on the premise that atmospheric states with similar initial conditions tend to evolve in a similar manner (Zhao et al., 2016; Lorenz, 1969). As a result, they provide an interpretable and computationally efficient forecasting approach. However, the high dimensionality of the predictor space, combined with the choice of similarity metric, makes the identification of relevant analogues for forecasting extreme events non-trivial.

By drawing on architectural principles from state-of-the-art deep learning-based weather forecasting models, we propose a novel forecasting method that combines traditional analogue techniques with self-supervised learning. Global atmospheric, ocean, and land surface fields are first mapped into a low-dimensional latent space. Analogues are then identified in this learned space, enabling probabilistic reconstruction and forecasting of heat extremes. We evaluate our method in terms of analogue selection and forecast accuracy, with a particular emphasis on interpretability, physical consistency, and generalization to unseen heat extremes.

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