Global warming is expected to change the regime of extreme precipitation. Physical laws translate increasing atmospheric heat into increasing atmospheric water content that drives precipitation changes. Within the literature, general agreement is that extreme precipitation is changing, yet different assessment methods, data sets, and study periods may result in different patterns and rates of change. Here we perform a global analysis of 8,730 daily precipitation records focusing on the 1964–2013 period when the global warming accelerates. We introduce a novel analysis of the N largest extremes in records having N complete years within the study period. Based on these extremes, which represent more accurately heavy precipitation than annual maxima, we form time series of their annual frequency and mean annual magnitude. The analysis offers new insights and reveals (1) global and zonal increasing trends in the frequency of extremes that are highly unlikely under the assumption of stationarity and (2) magnitude changes that are not as evident. Frequency changes reveal a coherent spatial pattern with increasing trends being detected in large parts of Eurasia, North Australia, and the Midwestern United States. Globally, over the last decade of the studied period we find 7% more extreme events than the expected number. Finally, we report that changes in magnitude are not in general correlated with changes in frequency.

The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (1) to provide tools that quantify the sampling uncertainty in popular performance metrics; (2) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (3) to prescribe the further research that is needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

The upper part of a probability distribution, usually known as the tail, governs both the magnitude and the frequency of extreme events. The tail behaviour of all probability distributions may be, loosely speaking, categorized into two families: heavy-tailed and light-tailed distributions, with the latter generating "milder" and less frequent extremes compared to the former. This emphasizes how important for hydrological design it is to assess the tail behaviour correctly. Traditionally, the wet-day daily rainfall has been described by light-tailed distributions like the Gamma distribution, although heavier-tailed distributions have also been proposed and used, e.g., the Lognormal, the Pareto, the Kappa, and other distributions. Here we investigate the distribution tails for daily rainfall by comparing the upper part of empirical distributions of thousands of records with four common theoretical tails: those of the Pareto, Lognormal, Weibull and Gamma distributions. Specifically, we use 15 029 daily rainfall records from around the world with record lengths from 50 to 172 yr. The analysis shows that heavier-tailed distributions are in better agreement with the observed rainfall extremes than the more often used lighter tailed distributions. This result has clear implications on extreme event modelling and engineering design.

Precipitation extremes are expected to intensify under climate change with consequent impacts in flooding and ecosystem functioning. Here we use station data and high‐resolution simulations from the WRF convection permitting climate model (∼4 km, 1 h) over the US to assess future changes in hourly precipitation extremes. It is demonstrated that hourly precipitation extremes and storm depths are expected to intensify under climate change and what is now a 20‐year rainfall will become a 7‐year rainfall on average for ∼ 75% of gridpoints over the US. This intensification is mostly expressed as an increase in rainfall tail heaviness. Statistically significant changes in the seasonality and duration of rainfall extremes are also exhibited over ∼ 95% of the domain. Our results suggest more non‐linear future precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year.