The role of global storm-resolving models (GSRM) in climate modeling is becoming increasingly important for accurate predictions of climate change and, in particular, its impacts on extreme weather. In Houston, deep convective storms frequently occur, making it an ideal region for evaluating deep convection simulations by GSRMs. This study uses observations from the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign to evaluate simulations of the Doubly Periodic (DP) and regionally refined (RRM) versions of the Simple Convection-Permitting E3SM Atmosphere Model (SCREAM) for a 10-day period of deep convective storms during the 2022 summer. Overall, the DP-SCREAM is skillful at predicting deep convective storms, accurately simulating the time series of surface precipitation rate and the accumulated precipitation, despite its idealized boundary conditions. However, the cloud liquid water path is significantly underestimated. Notably, the model accurately identifies days with high levels of liquid water content and ice water content which is necessary for an improved representation of cloud process. The scale-awareness of the SCREAM is examined by running the DP-SCREAM at various horizontal resolutions of 3 km, 5 km, and 11 km. The resolution sensitivity is small.