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The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (<i>P_maxp</i>_%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R²) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971&#8315;2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of <i>P_maxp</i>_% was determined based on the following probability distributions of random variables: Pearson&#8217;s type III (PIII), Weibull&#8217;s (W), log-normal, generalized extreme value (GEV), and Gumbel&#8217;s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.