Given recent scientific advances, coastal flooding events can be modelled even in complex environments. However, such models are computationally expensive, preventing their use for forecasting. At the same time, metamodelling techniques have been explored for coastal hydrodynamics, showing promising results. Developing such techniques for predicting coastal flood information (e.g., inland water depths) requires large enough learning datasets providing such inland information. However, detailed inland coastal flood observations are scarce and—when available—only correspond to a limited number of events. This paper aims at demonstrating how we can fill this gap by introducing a publicly available dataset, presenting its setup, and providing examples of use and recommendations. It has been built for the site of Gâvres (France), relying on the joint use of spectral wave (WW3) and non-hydrostatic wave-flow (SWASH) models, accounting for wave overtopping. It compiles 250 scenarios (defined by time-varying forcing conditions; including real and stochastically generated events) and the resulting maximal flooded areas and water depths (on 64,618 inland points). Its construction required the equivalent of 2 years of simulations on 48 cores. The examples of use of the dataset focus on method developments (metamodelling, forecast), local knowledge, and risk management.