Compound coastal flooding poses significant risks to urban and ecological systems, particularly under changing climate conditions. This paper synthesizes findings from a recent study that leveraged a hybrid statistical-dynamical modeling framework to investigate compound flooding in the San Francisco Bay Area. By generating over 4.3 million hourly total water levels (TWLs) across 100 simulations spanning 500 years, this framework fully quantifies and propagates uncertainties in forcing drivers, such as sea-level rise and river discharge, to predict extreme flooding events. Results reveal that despite the variability in individual forcing parameters, the range of return level events, such as 100-year TWLs, remains low across simulations, underscoring the robustness of the approach. This characteristic highlights the unique nature of compound flooding, where diverse forcing combinations can produce similar flood magnitudes. The framework’s computational efficiency allows for the exploration of a broader range of scenarios than traditional dynamical or statistical methods. Key findings emphasize the increasing frequency and magnitude of extreme TWLs under more severe climate scenarios, with implications for adaptation and resilience planning. This synthesis demonstrates the power of hybrid modeling approaches in capturing complex interactions driving compound flooding, providing critical insights for mitigating flood risks in vulnerable coastal regions.