Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products. This paper 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 utilises a novel two-stage training strategy to bridge the reanalysis-to-forecast domain shift. The model is first pre-trained on 40 years of ERA5-Land reanalysis (1980-2019) to capture robust hydrological processes, then fine-tuned on operational Integrated Forecasting System (IFS) control forecasts (2016-2019) 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 ecosystem. On an independent temporal test set (2021-2024), AIFL achieves high predictive skill with a median modified Kling-Gupta Efficiency (KGE') of 0.66 and a median Nash-Sutcliffe Efficiency (NSE) of 0.53. Benchmarking results show that AIFL is highly competitive with current state-of-the-art global systems, achieving comparable accuracy while maintaining a transparent and reproducible forcing pipeline. The model demonstrates exceptional reliability in extreme-event detection, providing a streamlined and operationally robust baseline for the global hydrological community.

翻译：可靠的全球径流预报对于防洪准备和水资源管理至关重要，然而数据驱动模型在从历史再分析过渡到业务预报产品时常常存在性能差距。本文提出了AIFL（面向洪水的人工智能），一种基于确定性LSTM的模型，专为全球日径流预报设计。该模型在CARAVAN数据集中精选的18,588个流域上进行训练，采用了一种新颖的两阶段训练策略以弥合再分析至预报的领域偏移。模型首先在40年的ERA5-Land再分析数据（1980-2019）上进行预训练，以捕捉稳健的水文过程；随后在业务化集成预报系统（IFS）控制预报数据（2016-2019）上进行微调，以适应业务数值天气预报特有的误差结构和偏差。据我们所知，这是在CARAVAN生态系统中首个端到端训练的全球模型。在独立的时间测试集（2021-2024）上，AIFL展现出较高的预测能力，其中位修正克林-古普塔效率（KGE'）达到0.66，中位纳什-萨特克利夫效率（NSE）达到0.53。基准测试结果表明，AIFL与当前最先进的全球系统相比具有高度竞争力，在保持透明且可复现的驱动流程的同时，达到了相当的精度。该模型在极端事件检测中表现出卓越的可靠性，为全球水文学界提供了一个精简且业务稳健的基准。