The October Through December "short rains" (OND) in East Africa show significant interannual variability. Long-range rainfall forecasts can help with preparation and readiness for such situations since drought and flooding are not uncommon occurrences. Although statistical models based on Sea Surface Temperature (SST) precursors are still extensively employed, and seasonal forecasts based on dynamical models are beginning to gain traction, it is crucial to comprehend the advantages and disadvantages of such models. Here, we define a straightforward statistical forecast model that may be used to provide light on the processes that connect SSTs and rainfall over time and space, as well as the reasons why these models occasionally fall short. The August states of the El Nio-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict roughly 40% of the variability in brief rains from 1950 to 2020 in our model, which is a linear regression. Forecasting mistakes can be connected linearly to initial positive (negative) ENSO and IOD conditions in August for too-wet (too-dry) forecasts. Changes in the IOD between August and OND serve as a mediator in the link to the initial IOD state, illuminating a possible physical mechanism for prediction busts. We also find asymmetry and nonlinearity: the range and variance of OND forecast errors are higher in August when ENSO and/or the IOD is positive than when the SST indices are negative.
