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Land cover and its associated biophysical parameters govern many land–atmosphere interactions. Several previous studies have demonstrated the utility of incorporating satellite-derived observations of land cover into climate models to improve prediction accuracy. In the developing world where agriculture is a primary livelihood, a better understanding of seasonal variability in precipitation and near-surface temperature is critical to constructing more effective coping strategies for climate changes and food security. However, relatively few studies have been able to assess the impacts of improved surface parameterisation on these variables and their seasonality. Using moderate resolution imaging spectroradiometer (MODIS)-derived products, we sought to address this shortcoming by adapting leaf area index (LAI) and vegetative fractional cover (FC) products, along with an improved representation of the land surface (i.e. land use land cover) into the Regional Atmospheric Modelling System in East Africa to evaluate the effect improved representations would have on simulated precipitation and land surface temperature (LST). In particular, we tested the hypothesis that improved phenological parameterisations could reduce error in precipitation and LST under dramatically different atmospheric conditions. The model was used to simulate dry/normal/wet rainfall years of 2000, 2001, and 2002 (respectively) in order to understand biases in this parameterisation under different boundary conditions. Our results show a dramatic improvement in LST simulation due to the use of the improved representations (spline functions) during most of the year, both spatially and temporally. Annual precipitation, which is dependent upon a much greater variety of surface and atmospheric characteristics, did not improve as much by adopting the spline representations of LAI and FC; the results were more equivocal. However, seasonal timing of precipitation improved in some areas, and this improvement has important consequences for integrated climate–agriculture assessments.