Flood events cause billions of dollars in economic losses annually, and these losses are projected to increase as precipitation intensifies and exposure expands. Linking observed flood losses to the statistical rarity of the triggering precipitation is essential for understanding current risk and for distinguishing between large losses triggered by very extreme events and those driven by more moderate events with high exposure. We will develop a modular framework that links pluvial and rain-triggered fluvial floods in Europe to the frequency of the precipitation events that caused them. The workflow integrates historical flood event catalogues and high-resolution gridded climate datasets. Each flood event, characterized by date, type, affected region, and reported losses, is assigned a spatial footprint, initially based on administrative units and refined using river basin information where available. Within these footprints, precipitation statistics (e.g. one-hour maximum rainfall) are extracted and converted into return periods. By systematically linking pluvial and rain-triggered fluvial flood damages to the return period of the triggering precipitation events, the framework can support more focused and effective flood risk management strategies.