Abstract
Maximum annual daily precipitation is a fundamental hydrologic variable that does not attain asymptotic conditions. Thus the classical extreme value theory (i.e., the Fisher-Tippett's theorem) does not apply and the recurrent use of the Generalized Extreme Value distribution (GEV) to estimate precipitation quantiles for structural-design purposes could be inappropriate. In order to address this issue, we first determine the exact distribution of maximum annual daily precipitation starting from a Markov chain and in a closed analytical form under the hypothesis of stochastic independence. As a second step, we formulate a superstatistics conjecture of daily precipitation, meaning that we assume that the parameters of this exact distribution vary from a year to another according to probability distributions, which is supported by empirical evidence. We test this conjecture using the world GHCN database to perform a worldwide assessment of this superstatistical distribution of daily precipitation extremes. The performances of the superstatistical distribution and the GEV are tested against data using the Kolmogorov-Smirnov statistic. By considering the issue of model's extrapolation, that is, the evaluation of the estimated model against data not used in calibration, we show that the superstatistical distribution provides more robust estimations than the GEV, which tends to underestimate (7-13%) the quantile associated to the largest cumulative frequency. The superstatistical distribution, on the other hand, tends to overestimate (10-14%) this quantile, which is a safer option for hydraulic design. The parameters of the proposed superstatistical distribution are made available for all 20,561 worldwide sites considered in this work.