This study considered the impacts of time-step size and spatial resolution on the prediction skill of the Global/Regional Assimilation and Prediction System(GRAPES) mesoscale numerical forecast system(GRAPES-MESO) for a given parameter set. The forecasts of geopotential height(H), temperature(T), and the zonal(U) and meridional(V) components of wind at 700, 500, and 200 hPa, were assessed, as well as surface precipitation. The results showed that, at a spatial resolution of 0.3°×0.3°, the prediction skill of almost all variables, including H, T, U and V, in the three vertical layers were optimized at a particular time step of approximately 240 s. This raises the possibility of an optimal time-step size for a particular spatial resolution, and the explanation for this relationship might be related to the computational uncertainty principle. The operational forecasts based on a spatial resolution of 0.15°×0.15° and a time-step size of 90 s were also compared with the best results obtained previously, in which the spatial resolution was 0.3°×0.3° and the time step was 240 s. The latter configuration possessed higher skill than the operational forecasts for all variables, indicating that the prediction quality may not be significantly improved by an increase in the spatial resolution of the model.