Coupling Precipitation Forecasting and Early Warning with Reverse-Martingale Recurrent Neural Networks

Precipitation forecasts are judged by accuracy, but the decisions they support -- when to restrict water, when to warn of drought -- turn on noticing when a local regime is becoming abnormal, which forecast scores alone do not reveal.

We ask whether one recurrent model can do both with little or no loss in forecast skill.

We add a backward-coherence (reverse-martingale) penalty that keeps the network's hidden state smooth when read backward in time; the size of the resulting reconstruction defect becomes an online warning signal, monitored by a sequential change-point detector.

The design is deliberately conservative.

On real daily station data from four contrasting climates -- monsoonal Taiwan, semi-arid Texas, temperate Germany, and Mediterranean Anatolia (Turkey) -- the model matches a standard network's forecast skill everywhere, and makes the hidden state markedly steadier in every region.

The novelty is the added information: on these real droughts the signal can alarm well ahead of the operational SPI-3 index, giving lead that neither the forecast nor the index provides.

This benefit is not uniform across the four regions -- large in one, partial in two others, and near-absent in the fourth.

We offer the hydroclimatic character of drought onset, whether it precedes or merely coincides with the rainfall deficit, as a plausible explanation to be tested in future work, supported by a controlled synthetic study with known onset times.

The contribution is thus a new and conservative way to read precipitation records: no loss in forecast skill, a steadier model, and an early-warning signal beyond the standard index.