AIFL: A New Global Flood Forecasting Model Trained on CARAVAN

Reliable global streamflow forecasting is essential for flood preparedness, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products. This study introduces AIFL (Artificial Intelligence for Floods), a deterministic LSTM-based model designed for global daily streamflow forecasting. Trained on 18,588 basins curated from the CARAVAN dataset, AIFL utilizes a novel two-stage training strategy to bridge the reanalysis-to-forecast domain shift. The model is first pretrained on 40 years of ERA5 reanalysis to capture robust hydrological processes, then fine-tuned on operational Integrated Forecasting System (IFS) control forecasts to adapt to the specific error structures and biases of operational numerical weather prediction. To our knowledge, this is the first global model trained end-to-end within the CARAVAN and MultiMet ecosystem. Evaluated on an independent temporal test set (2021–2024), AIFL achieves a median KGE’ of 0.66 and a median NSE of 0.53. Benchmarking results show that AIFL is highly competitive with current state-of-the-art global systems, maintaining a transparent and reproducible forcing pipeline while demonstrating exceptional reliability in extreme event detection. The resulting model provides a streamlined and operationally robust baseline for the global hydrological community.