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SEEPS4ALL [1] combines a precipitation dataset in a Zarr format and a set of verification Jupyter Notebooks for the evaluation of daily precipitation forecasts over Europe. The dataset is primarily based on daily in-situ observations from the European Climate Assessment & Dataset project (www.ecad.eu). Climate statistics are derived from long time series at each station location to enable the computation of meaningful verification metrics. For example, the Stable and Equitable Error in Probability Space (SEEPS [2]) is a score specifically designed to assess the performance of precipitation forecasts, and it requires climate statistics.The verification notebooks showcase the computation not only of SEEPS but also of the diagonal score (the equivalent of SEEPS for probabilistic forecasts) and of the brier score as a function of climate percentiles. Finally, when comparing a gridded forecast and a point observation, one can account for observation representativeness uncertainty by dressing the forecast with pre-defined scale-dependent parametric distributions [3]. In a nutshell, SEEPS4ALL helps promote the benchmarking of daily precipitation forecasts against in-situ observations over Europe. [1] Ben Bouallègue Z, A. Prieto-Nemesio, A.I. Wong, F. Pinault, M. van der Schee, and U. Modigliani (2025), SEEPS4ALL: an open dataset for the verification of daily precipitation forecasts using station climate statistics. Earth System Science Data, https://doi.org/10.5194/essd-2025-553[2] Rodwell, M.J., D.S. Richardson, T.D. Hewson and T. Haiden (2010), A new equitable score suitable for verifying precipitation in numerical weather prediction. Q.J.R. Meteorol. Soc., https://doi.org/10.1002/qj.656[3] Ben Bouallègue, Z., T. Haiden, N. J. Weber, T. M. Hamill, and D. S. Richardson (2020), Accounting for Representativeness in the Verification of Ensemble Precipitation Forecasts. Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-19-0323.