Precipitation nowcasting is critically important for meteorological forecasting. Deep learning-based Radar Echo Extrapolation (REE) has become a predominant nowcasting approach, yet it suffers from poor generalization due to its reliance on high-quality local training data and static model parameters, limiting its applicability across diverse regions and extreme events. To overcome this, we propose REE-TTT, a novel model that incorporates an adaptive Test-Time Training (TTT) mechanism. The core of our model lies in the newly designed Spatio-temporal Test-Time Training (ST-TTT) block, which replaces the standard linear projections in TTT layers with task-specific attention mechanisms, enabling robust adaptation to non-stationary meteorological distributions and thereby significantly enhancing the feature representation of precipitation. Experiments under cross-regional extreme precipitation scenarios demonstrate that REE-TTT substantially outperforms state-of-the-art baseline models in prediction accuracy and generalization, exhibiting remarkable adaptability to data distribution shifts.

翻译：临近降水预报对气象预测至关重要。基于深度学习的雷达回波外推已成为主流的临近预报方法，但由于其依赖高质量本地训练数据和静态模型参数，泛化能力较差，限制了其在多样化区域和极端事件中的适用性。为解决这一问题，我们提出了REE-TTT，一种融合自适应测试时训练机制的新型模型。该模型的核心在于新设计的时空测试时训练模块，该模块将TTT层中的标准线性投影替换为面向任务的注意力机制，从而能够稳健适应非平稳气象分布，显著提升降水特征的表征能力。跨区域极端降水场景下的实验表明，REE-TTT在预测精度和泛化能力上大幅优于现有基线模型，对数据分布偏移表现出卓越的适应能力。