This study produces wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) by leveraging regional mesh refinement over California (CARRM) simulations at 3.25 km and 800 m horizontal resolutions, using the Python wrapper of System Advisor Model (PySAM). The resulting wind and solar energy generation estimates are compared to monthly capacity factors from the Energy Information Administration (EIA), the High-Resolution Rapid Refresh (HRRR; 3 km resolution) forecast model, and the E3SM North American Regionally Refined Model (NARRM; 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 km to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM’s performance for solar generation surpasses HRRR, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of generation modeling uncertainties.