Urban areas (i.e. cities, towns and suburbs) provide a home to over 70% of the EU‑population, and this number is expected to exceed 80% by 2050 (Tapia et al., ECOL INDIC, 2017). The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g. cloudbursts, rainstorms, heavy rainfall, hail, heavy snow) combined with the high population density and concentration of assets in urban areas makes them particularly vulnerable to pluvial flooding, hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modelling is resource intensive and therefore scarcely suitable for a consistent hazard assessment across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential for consistent pluvial flood hazard characterization of fast-processing DEM-based methods, such as the Hierarchical Filling and Spilling or Puddle-to-Puddle Dynamic Filling and Spilling (see e.g. Zhang et al., J HYDROL, 2014; Chu et al., WATER RESOUR RES, 2013). As part of the activities of the EIT Climate-KIC Demonstrator project SAFERPLACES (https://saferplaces.co/), we developed a fast-processing algorithm, named Safer_RAIN, that enables one to map pluvial flooding in large urban areas by implementing a filling and spilling procedure that accounts for spatially distributed rainfall input and infiltration processes (Green Ampt method). We present the first applications of the algorithm to model recent urban inundations occurred in Northern Italy. These preliminary applications, compared against ground evidence and detailed output from a two-dimensional hydrologic and hydraulic numerical model, highlight limitations and potential of Safer_RAIN for identifying pluvial-hazard hotspots across large urban environments.