Spatially explicit large-scale crop growth models are often applied at the global scale with no or little adjustments to regional conditions, which may produce unreliable model results. To tackle this issue, we have regionalized a large-scale crop model for simulating maize cultivation in sub-Saharan Africa (SSA). Planting dates were estimated using reported planting seasons, plant growth parameters were adopted from literature to reflect a low-yielding cultivar, and agricultural practice was mimicked by simulating continuous cultivation of maize with removal of plant residues. The analysis of different estimates of planting date showed that a monthly time step was too coarse in (semi-)arid regions and a weekly step should be used. Limiting planting date estimates by reported seasons is especially important in regions with bimodal rain seasons. The parameterization of a low-yielding cultivar by decreasing the maximum and minimum harvest index (HI) in the model resulted in HI estimates within the range of values reported in the literature. The most important step in the model adjustment was found to be the removal of plant residue. This leads together with little fertilizer inputs to soil nutrient and organic carbon depletion, which has been taking place in most parts of SSA during the past decades. If residue removal is not taken into account, the simulation results in organic carbon sequestration and only minor nutrient depletion. With the adjustments of cultivar, planting dates, and agricultural practice in the model setup, crop growth is in most areas of SSA mainly constrained by nutrient stress as compared to water and temperature. The estimated national and regional average yields compared well with reported yields for the major maize producing countries, suggesting that the regionalized model is suitable for supporting policies on water and soil management in SSA.