To facilitate reproducible hydrological research and support model testing and evaluation, several datasets gathering hydroclimatic information on large catchment samples have been released in different regions of the world over the last years. Addor et al. (2017) proposed a large dataset of Catchment Attributes and Meteorology for Large-sample Studies (CAMELS), consisting of 671 catchments in the contiguous United States. Then other datasets were produced in the CAMELS framework in Great Britain (CAMELS-GB, 671 catchments), Chile (CAMELS-CL, 516 catchments), Brazil (CAMELS-BR, 897 catchments) and Australia (CAMELS-AUS, 222 catchments). They consist of catchment hydro-meteorological time series and catchment attributes. This presentation aims at introducing CAMELS-FR, a dataset on French catchments that was set up for hydrological studies. This dataset has been assembled at INRAE, France, based on an automatized processing chain of national data products (Delaigue et al. 2020), among which are the SAFRAN atmospheric reanalysis produced by Météo-France (Vidal et al., 2010) and the national river flow archive (Banque HYDRO), maintained by the French centre for flood forecasting (SCHAPI). CAMELS-FR provides daily catchment-scale hydrometeorological time series (streamflow, solid and liquid precipitation, potential evapotranspiration, temperature, etc.) covering the 1958-2020 period. Catchment characteristics such as land cover, topography (i.e. elevation and slope distributions, drainage density, topographic index, etc.) are also provided, with information about possible regulations upstream, and with some basic information on data quality. Graphical summary sheets provide synthetic information on the main characteristics of each catchment. The performance of several lumped rainfall-model models applied on the CAMELS-FR dataset will be presented, in order to highlight potential uses of this dataset for modeling applications. The CAMELS-FR dataset will be made freely available to the scientific community in partnership with data providers (Météo-France and SCHAPI).