Rainfall variability is a critical constraint to crop production in Sub-Saharan Africa (SSA), where only 4% of cultivated areas are equipped for irrigation. Improved understanding of the dominant patterns of rainfall and crop yield variability can contribute to the formulation of better agricultural investment and food security strategies and to better investment targeting. Using a recently released, long-term historic climatic database at high spatial-resolution and a process-based crop systems model operating on 10 km grids, a cropping systems simulation framework was developed for SSA by the HarvestChoice initiative and was applied to assess both spatial and temporal patterns of rainfall and yield variability in the SSA region. First, the spatio-temporal patterns of seasonal rainfall variability were assessed in order to characterize rainfall patterns in major crop growing areas in SSA. Second, a cropping systems model was used to translate the effects of rainfall variability on the variability of major cereal crop yields. Lastly, potential pathways for increasing mean crop yields while reducing year-to-year yield variability were examined using the same crop model. The analysis showed that the majority of crop growing areas in the region have relatively high seasonal rainfall variability. The variability of simulated crop yield across the region largely followed the same pattern as rainfall variability, but the magnitude of that variability differed depending on other agronomic factors. Various management options were simulated to assess the most effective strategies for reducing crop yield variability for each location. A combination of nutrient and water management practices were shown to deliver the best overall agronomic outcomes. The economic aspects of adopting the recommended practices are being investigated in on-going studies. Overall, this new grid-based research framework shows great potential for assessing the distribution and scale of crop yield variability due to rainfall variability and for identifying variability mitigation strategies using improved technologies and practices.