Short-term global ensemble predictions of rainfall currently have no
skill over northern tropical Africa when compared to simple
climatology-based forecasts, even after sophisticated statistical
postprocessing. Here we demonstrate that statistical forecasts for the
probability of precipitation based on a simple logistic regression model
have considerable potential for improvement. The new approach we present
here relies on gridded rainfall estimates from the Tropical Rainfall
Measuring Mission for July--September 1998--2017 and uses rainfall
amounts from the pixels that show highest positive and negative
correlations on the previous two days as input. Forecasts using this
model are reliable and have a higher resolution and better skill than
climatology-based forecasts. The good performance is related to westward
propagating African easterly waves and embedded mesoscale convective
systems. The statistical model is outmatched by the postprocessed
dynamical forecast in the dry outer tropics only, where extratropical
influences are important.