Weather Research and Forecast (WRF) model exploratory sensitivity simulations were performed to determine the impact of vertical grid resolution (VGR) on the forecast skill of Midwest summer rainfall. Varying the VGR indicated that a refined VGR, while adopting the widely used North America Regional Reanalysis (NARR) for initial and lateral boundary conditions, does not necessarily result in a consistent improvement in quantitative precipitation forecasts (QPFs). When averaged over a variety of microphysical schemes in an illustrative case, equitable threat score (ETS) and bias values actually worsened with a greater overpredicted rainfall for half of the rainfall thresholds when the VGR was refined. Averaged over strongly forced cases, ETS values worsened for all rainfall thresholds while biases mostly increased, indicating a further overprediction of rainfall when the number of levels was increased. Skill improved, however, for all rainfall thresholds when the resolution above the melting level was increased. Skill also improved for most rainfall thresholds when the resolution in the surface layer was increased, which is attributed to better-resolved surface turbulent momentum and thermal fluxes. Likewise, a refined VGR resulted in improvements in weakly forced cases, which are governed mostly by thermodynamic forcing and are sensitive to vertical profiles of temperature and moisture. Application of the factor separation method suggested that the refined VGR more frequently had a negative impact on skill through the interaction between lower-atmospheric processes and microphysical processes above the melting level.