Accurate precipitation nowcasting is crucial for disaster mitigation and socio-economic planning, yet existing methods often struggle with false alarms, missed events, and long range dependency modeling at high spatiotemporal resolution. To address these challenges, we propose FlashBack Memory (FB), a module that dynamically retrieves key historical states and integrates them via an adaptive fusion gate, enhancing the spatiotemporal representation capability of recurrent-based models. We incorporate FB into PredRNN, PredRNNpp, MIM, MotionRNN, and PredRNN-V2, and evaluate on CIKM2017, Shanghai2020, and SEVIR datasets. Experimental results demonstrate that FB significantly improves MSE, MAE, SSIM, and CSI metrics, particularly for high-intensity rainfall and long-sequence predictions, while reducing false alarms and missed events and enhancing temporal consistency and spatial localization. The proposed method provides a general and efficient memory enhancement mechanism, improving the overall performance of recurrent-based precipitation nowcasting models.

翻译：精准的降水临近预报对防灾减灾和社会经济规划至关重要，然而现有方法在高时空分辨率下常面临虚报、漏报以及长程依赖建模的挑战。针对这些问题，我们提出FlashBack记忆模块（FB），该模块动态检索关键历史状态并通过自适应融合门进行整合，增强基于循环模型的时空表征能力。我们将FB集成到PredRNN、PredRNNpp、MIM、MotionRNN和PredRNN-V2中，并在CIKM2017、Shanghai2020和SEVIR数据集上进行评估。实验结果表明，FB显著提升了MSE、MAE、SSIM和CSI指标，尤其在高强度降雨和长序列预测场景中效果突出，同时减少了虚报和漏报事件，增强了时间一致性和空间定位精度。所提方法提供了一种通用且高效的内存增强机制，全面改善了基于循环的降水临近预报模型的整体性能。