Forecasting extreme temperature events by Hankel-augmented contrastive learning

Accurate forecasting of extreme temperature events is critically important for climate adaptation and disaster preparedness, yet it remains a fundamental challenge due to the abrupt and non-stationary nature of such phenomena. Current time-series models often fail to capture rapid transitions and anomalous patterns that deviate significantly from historical behaviors. Here, we propose Hankelformer, a novel deep learning architecture that integrates structured Hankel-based augmentation with contrastive learning to significantly improve the forecasting of extreme weather events. The model employs a structured augmentation module that constructs Hankel matrices to capture local spatiotemporal dynamics without disrupting temporal coherence, generating delay-embedding-inspired views of the original input data in terms of states. These views are processed together with the original input in a dual-stream Transformer encoder, followed by a contrastive learning between them that forms spatiotemporal representations, thus significantly enhancing feature invariance and robustness against distribution shifts. We evaluated Hankelformer on the curated extreme weather datasets: the TexasFreeze, the Pacific Northwest Heatwave and the Antarctic Heat, in addition to six standard benchmarks in energy and transportation. Hankelformer consistently achieves state-of-the-art performance, demonstrating remarkable accuracy in predicting extreme temperature collapses and spikes, with up to 34% improvement in mean squared error over leading baselines. The framework offers a promising tool for reliable extreme temperature forecasting and underscores the value of constructing topologically equivalent sequences to the original sequence for spatiotemporal representation learning in handling real-world non-stationary time series.