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The problem of high-quality drought forecasting up to a year in advance iscritical for agriculture planning and insurance. Yet, it is still unsolved withreasonable accuracy due to data complexity and aridity stochasticity. We tackledrought data by introducing an end-to-end approach that adopts aspatio-temporal neural network model with accessible open monthly climate dataas the input.  Our systematic research employs diverse proposed models and five distinctenvironmental regions as a testbed to evaluate the efficacy of the PalmerDrought Severity Index (PDSI) prediction. Key aggregated findings are theexceptional performance of a Transformer model, EarthFormer, in making accurateshort-term (up to six months) forecasts. At the same time, the ConvolutionalLSTM excels in longer-term forecasting. Both models achieved high ROC AUCscores: 0.948 for one month ahead and 0.617 for twelve months ahead forecasts.

Long-term drought prediction using deep neural networks based on geospatial weather data