Accurately predicting the extent of compound flooding events, including storm surge, pluvial, and fluvial flooding, is vital for protecting coastal communities. However, high computational demands associated with detailed probabilistic models highlight the need for simplified models to enable rapid forecasting. The objective of this study was to assess the accuracy and efficiency of a reduced-complexity, hydrodynamic solver – the Super-Fast INundation of CoastS (SFINCS) model – in a probabilistic ensemble simulation setting, using Hurricane Ike (2008) in the Texas Gulf Coast as a case study. Results show that the SFINCS-based framework can provide probabilistic outputs under reasonable simulation times (e.g., less than 4 hours for a 100-member ensemble on a single CPU). The model agrees well with observed data from NOAA tidal stations and USGS gage height stations. The ensemble approach significantly reduced errors (average 16%) across all stations compared to a deterministic case. The ensemble improved overall performance and revealed wider flood extents and lower depths. Sensitivity studies performed on ensemble sizes (1,000, 189, 81) and lead times (1 to 3 days before landfall) further demonstrate the reliability of flood extent predictions over varying lead times. In particular, Counties adjacent to the Trinity River Basin had ≥ 80% probability in exceeding the critical 3-m flood threshold during Hurricane Ike. Our study highlights the effectiveness of the SFINCS-based framework in providing probabilistic flood extent/depth forecasts over long lead times in a timely manner. Thus, the framework constitutes a valuable tool for effective flood preparedness and response planning during compound flooding.