Effective flash flood forecasting and risk communication are imperative for mitigating the impacts of flash floods. However, the current forecasting of flash flood occurrence and magnitude largely depends on forecasters’ expertise. An emerging flashiness-intensity-duration-frequency (F-IDF) product is anticipated to facilitate forecasters by quantifying the frequency and magnitude of an imminent flash flood event. To make this concept usable, we develop two distributed F-IDF products across the contiguous US, utilizing both a Machine Learning (ML) approach and a physics-based hydrologic simulation approach that can be applied at ungaged pixels. Specifically, we explored 20 common ML methods and interpreted their predictions using the Shapley Additive exPlanations method. For the hydrologic simulation, we applied the operational flash flood forecast framework – EF5/CREST. It is found that: (1) both CREST and ML depict similar flash flood hot spots across the CONUS; (2) The ML approach outperforms the CREST-based approach, with the drainage area, air temperature, channel slope, potential evaporation, soil erosion identified as the five most important factors; (3) The CREST-based approach exhibits high model bias in regions characterized by dam/reservoir regulation, urbanization, or mild slopes. We discuss two application use cases for these two products. The CREST-based approach, with its dynamic streamflow predictions, can be integrated into the existing real-time flash flood forecast system to provide event-based forecasts of the frequency and intensity of floods at multiple durations. On the other hand, the ML-based approach, which is a static measure, can be integrated into a flash flood risk assessment framework for urban planners.