Uncertainty-Aware Prediction of Climate Extremes Using Fine-Tuned Time-Series Foundation Models

AI-driven weather forecasting models, particularly foundation models, have achieved significant advancements in both speed and accuracy. However, accurately forecasting rare, high-impact extreme events, such as storms and heatwaves, remains a critical challenge. These models often underestimate event intensity and frequency, limiting their reliability in operational and risk-sensitive contexts. In this study, we investigate uncertainty-aware extreme event forecasting using the recently introduced time-series foundation model, Tiny Time Mixers (TTM). We develop and compare two uncertainty quantification approaches, hyperparameter ensembling and Monte Carlo (MC) dropout, and evaluate their ability to improve classification of extreme events. Our results show that incorporating predictive uncertainty significantly enhances performance compared to zero-shot TTM, and that the choice of uncertainty method and threshold critically affects model behavior. We find that the hyperparameter ensemble yields more stable and accurate predictions, particularly for rare storm events, highlighting the value of lightweight ensemble models for uncertainty-calibrated forecasting.