Dimensionless groups of parameters characterizing an ecosystem are valuable indicators for the a priori assessment of the effect of rainfall data resolution on predictions of soil moisture and transpiration. Knowledge of these dimensionless groups enables identification of appropriate levels of rainfall data resolution, when using historical rainfall directly or when using it to derive rainfall model parameters for use in models of soil-plant-climate systems. Detailed simulation studies of the soil, plant, and climate systems in Colorado and Texas, highly resolved in time and vertical space, show that historical rainfall data resolved at the daily level allow accurate prediction of soil-moisture and transpiration dynamics for smaller time resolutions. These results support inferences based on the dimensionless groups. Furthermore, no significant improvement in the prediction of soil-moisture and transpiration dynamics is attained, when representing rainfall through a more complex Neyman-Scott model rather than the simple rectangular pulses Poisson model.