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Abstract

Floods are generally considered as one of the most significant extreme events in terms of casualties and losses. Although they are a direct consequence of weather and climate extremes, a full understanding of the influence of climate change on their socioeconomic impact requires the consideration of the human component in terms of exposure and vulnerability. At the same time, a global view of flood risk based on large catalogues of physically consistent events is of key interest to environmental research, climate science, economics, and financial risk management. However, the lack of flexibility to account for socioeconomic variables and the high computational cost to produce large global event sets of flood impact for future climate scenarios make the use of regional hydrological models unpractical. As an alterative, in this work we use the global flood modeling framework developed by Carozza and Boudreault (C&B) (Carozza & Boudreault, 2021).The C&B model applies statistical and machine learning methods to relate historical flood occurrence and impact data with climatic, watershed, and socioeconomic factors for 4,734 basins at Pfafstetter level 5 globally. The model is climate‐consistent, global, fast, flexible, and ideal for applications that do not necessarily require high‐resolution flood mapping. After training with observational data in the period 1986-2017, the climate variables are replaced with bias-corrected output from the NCAR CESM Large Ensemble (40 members) to project flood impact up to 2060. We produce a variety of event sets to assess how climate change and future socioeconomic growth may affect flood impact.