  The probability of a drought for a particular region is crucial when making
decisions related to agriculture. Forecasting this probability is critical for
management and challenging at the same time. The prediction model should
consider multiple factors with complex relationships across the region of
interest and neighbouring regions.
  We approach this problem by presenting an end-to-end solution based on a
spatio-temporal neural network. The model predicts the Palmer Drought Severity
Index (PDSI) for subregions of interest. Predictions by climate models provide
an additional source of knowledge of the model leading to more accurate drought
predictions.
  Our model has better accuracy than baseline Gradient boosting solutions, as
the $R^2$ score for it is $0.90$ compared to $0.85$ for Gradient boosting.
Specific attention is on the range of applicability of the model. We examine
various regions across the globe to validate them under different conditions.
  We complement the results with an analysis of how future climate changes for
different scenarios affect the PDSI and how our model can help to make better
decisions and more sustainable economics.

Machine Learning