Global climate models (GCMs) produce large errors in cloudiness and cloud radiative forcing when simulating midlatitude, synoptic-scale cloud systems. This is because they do not represent the subgrid-scale processes in these systems that create subgrid variability in cloud optical thickness and cloud top pressure. Improving GCM performance will require a better understanding of these controls on subgrid cloud variability. To begin addressing this issue, this paper uses a mesoscale model, the Regional Atmospheric Modeling System (RAMS), to simulate two case study synoptic storms with much higher resolution than is possible in a GCM. These storms were observed during the Atmospheric Radiation Measurement (ARM) Program's March 2000 Intensive Observing Period (IOP) in the U.S. southern Great Plains (SGP), otherwise knows as ARM case 4. We find that RAMS is able to capture the observed storm morphology, lifecycle, and vertical structure of the atmospheric dynamic and thermodynamic variables. RAMS is also able to capture the observed fine-scale vertical structure and temporal variation of the cloud field. Given this agreement with observations, we then characterize the model-simulated variability in cloudiness and other variables such as vertical velocity. In both storms, there is a high degree of spatial and temporal variability in the vertical motion field across multiple scales. The variability in above-boundary layer cloudiness is closely linked to this dynamical variability. This suggests that a parameterization for subgrid cloud water based on subgrid vertical velocity could be used to improve GCM simulations of midlatitude clouds.