ArticleiScience2026
DCSwinLSTM for spatiotemporal meteorological drought forecasting.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Meteorological drought prediction is critical for early warning and climate-risk management; yet, modeling regional drought evolution across multi-timescale remains challenging. Here, we propose DCSwinLSTM, a feature-fusion framework that combines deformable convolution for boundary-sensitive spatial encoding, a Swin transformer for hierarchical multi-scale feature learning, and an LSTM recurrent update for temporal dependency modeling. Using a global gridded dataset (1959-2022) constructed from the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) at 3- and 6-month scales, DCSwinLSTM consistently outperforms mainstream spatiotemporal prediction baselines. On the held-out SPI-3 test set, it achieves mean square error (MSE) 0.4269, mean absolute error (MAE) 0.5050, root mean square error (RMSE) 0.6534, R
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