Estimates of flood inundation from tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced-complexity flood inundation models have been previously shown to efficiently simulate the drivers of TC flooding across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a reduced-complexity hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high-resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). We compare modeled water levels to traditional validation datasets (e.g., water level gages, high-water marks) as well as property-level records of insured damage to draw conclusions about the model’s performance. We demonstrate that SFINCS can accurately simulate coastal and runoff drivers of TC flooding at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to the individual and compound flood drivers during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models that are capable of realistically representing the compound effects resulting from coastal and runoff processes.