Small-area precipitation forecasts support real-time decisions for reservoir operation, irrigation planning, drought monitoring, and flash-flood response. Operational value depends not only on point accuracy, but also on calibrated exceedance probabilities and warning rules that remain stable when local weather regimes depart from the training climatology. We evaluate a reverse-martingale regularized recurrent neural network (\RMRNN) for probabilistic precipitation forecasting and sequential early warning. A backward-coherence penalty is added to the recurrent hidden state; the resulting residual process drives a Shiryaev--Roberts (SR) detector, so the same latent trajectory that produces the forecast also supplies a continuously updated drought or flood-regime indicator. The framework is tested on the Taiwan CWA dense rain-gauge network, CHIRPS v2 daily gridded precipitation over Taiwan and the Horn of Africa, and NOAA GHCN-Daily stations over the Texas Hill Country. Across 1{,}000 replications, \RMRNN{} matches or slightly improves the GRU baseline in RMSE, MAE, and CRPS at 1~h--72~h lead while substantially improving alarm characteristics. The SR detector reduces false-alarm ratios by a factor of three to five at matched detection power. In the 2020--2021 Taiwan drought, onset is flagged 8--12 days earlier than SPI-3 thresholding; in the 2023 Typhoon Haikui flood, flash-flood risk is signalled 4~h before the CWA operational alert.

翻译：小区域降水预报可为水库调度、灌溉规划、干旱监测及山洪响应提供实时决策支持。其业务价值不仅取决于点预测精度，更依赖于校准的超越概率以及当局部天气状况偏离训练气候态时仍保持稳定的预警规则。我们评估了一种基于逆鞅正则化的循环神经网络（RMRNN）用于概率降水预报及序列式早期预警。该方法在循环隐状态中添加了后向一致性惩罚项；由此产生的残差过程驱动Shiryaev-Roberts检测器，使得生成预报的同一潜在轨迹能够同时提供持续更新的旱涝状态指标。该框架在台湾气象署密集雨量站网、台湾与非洲之角地区的CHIRPS v2日尺度网格化降水数据，以及德克萨斯丘陵地区的NOAA GHCN-Daily站点数据上进行了测试。在1000次重复试验中，RMRNN在1小时至72小时预见期内，其RMSE、MAE和CRPS指标与GRU基线相当或略有提升，同时显著改善了报警特性。在相同检测功效下，SR检测器将虚报比率降低了三至五倍。在2020—2021年台湾干旱事件中，该方法比SPI-3阈值法提前8—12天标记出干旱开始时间；在2023年海葵台风洪水事件中，其比气象署业务预警提前4小时发出山洪风险信号。