Uncertainty-Aware AI Forecasting of European Droughts: The Role of Internal Climate Variability

Accurate subseasonal forecasting of drought indices across spatial and temporal domains in Europe remains a major challenge due to internal climate variability, the inherent uncertainty in AI-driven forecasts, and complex atmospheric interactions. These challenges are particularly pronounced for rare and severe drought events, which can have substantial societal and environmental consequences. Recent advances in machine learning have improved climate forecasting, but the contribution of internal climate variability to predictive uncertainty in drought forecasts remains insufficiently quantified.

This study investigates whether observed limitations in the predictive performance of AI-based subseasonal drought forecasts can be explained by internal climate variability. To address this, we develop a Temporal Fusion Transformer framework to forecast the Standardized Precipitation–Evapotranspiration Index for a single month (SPEI-1) over the European domain. We extract the internal variability of a regional climate model large ensemble and quantify the extent to which predictive imprecision is attributed to internal climate variability. This approach enables a systematic assessment of hot and dry extremes, forecast skill, and uncertainty characterization.

The proposed approach enhances existing forecasting methods, particularly in terms of uncertainty quantification and its effective communication. The Temporal Fusion Transformer captures key temporal and spatial characteristics of SPEI-1 variability across Europe, except for limitations over the complex terrain of the Alps. Analysis of forecast variability shows that a substantial fraction of predictive uncertainty can be attributed to internal climate variability rather than model deficiencies alone. 

The interpretable uncertainty bounds provide a tool supporting risk assessment and decision-relevant drought forecasting, because they highlight the important role of internal climate variability for drought prediction. Overall, this work emphasizes how merging AI-driven forecasting techniques with quantification of internal climate variability can support more reliable and decision-relevant assessments of drought risk.