What Drives Online Adaptation of Frozen Forecasters? A Controlled Study of Drought-Index Nowcasting

Drought estimates depend on both recent weather and earlier dry or wet conditions. An online adapter can use these observations to improve a pretrained forecast, but that improvement does not reveal how much the forecast itself contributes. We study this question through retrospective drought-index nowcasting. Our evaluation compares three ways to use the same local observations: predict the index directly, correct a frozen forecast, or learn how much weight to give that forecast. A separate comparison holds the local features and update rules fixed, so forecast weighting can be examined without changing feature selection. Direct prediction explains much of the gain from fixed residual correction, while learning the forecast's weight improves further in both comparisons. We distinguish decisions made at each monthly step from decisions about where to apply adaptation. Historical errors help prioritize cases under a limited adaptation budget, but validation favors adapting all evaluated cases; reversion rarely changes predictions in the natural replay. These findings identify when a frozen forecast adds value beyond local learning in this retrospective setting.