Improving Estimates of Heavy and Extreme Precipitation Using Daily Records from European Rain Gauges

Abstract

Abstract The long-term variability in heavy precipitation characteristics over Europe for the period 1950–2000 is analyzed using high-quality daily records of rain gauge measurements from the European Climate Assessment (ECA) dataset. To improve the accuracy of heavy precipitation estimates, the authors suggest estimating the fractional contribution of very wet days to total precipitation from the probability distribution of daily precipitation than from the raw data, as it is adopted for the widely used R95tot precipitation index. This is feasible under the assumption that daily precipitation follows an analytical distribution like the gamma probability density function (PDF). The extended index R95tt based on the gamma PDF is compared to the classical R95tot index. The authors find that R95tt is more stable, especially when precipitation extremes are estimated from the limited number of wet days of seasonal and monthly time series. When annual daily time series are analyzed, linear trends in R95tt and R95tot are qualitatively consistent; both hint at a growing occurrence of extreme precipitation of up to 3% decade−1 in central western Europe and in south European Russia, with a somewhat more evident trend pattern for the R95tt index. Linear trends estimated for individual seasons, however, exhibit pronounced differences when derived from both indices. In particular, in winter, R95tt clearly reveals an increasing occurrence of extreme precipitation in western European Russia (up to 4% decade−1), while during summer, a downward tendency in the fractional contribution of very wet days is found in central western Europe. The new index also allows for a better association of European extreme precipitation with the North Atlantic Oscillation (NAO) index by showing a more consistent spatial correlation pattern and higher correlation levels compared to R95tot.