In this presentation, we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985‐2017), and climatic, watershed, and socioeconomic factors for 4734 watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980‐2020), and the fitted statistical relationships, we simulate one million years of events worldwide along with the population displaced in each event. During the presentation, we discuss potential applications of the model, notably for the international (re)insurance industry, including global flood hazard and risk maps, the impacts of El Nino on flood risk and the contribution of climate (change) and urbanization to flood risk over the past 40 years.