Trends in precipitation variability over Europe on different datasets

Different types of meteorological data (station, gridded, reanalysed) are known to have different statistical characteristics for higher order moments, extremes and trends. While the analysis of long-term changes in mean values and extremes is often studied, changes in precipitation variability are rather on the fringes of attention. We use data from stations across Europe (ECA&D project), gridded data (E-OBS, Regen), and several reanalyses (NCEP/NCAR, 20CR, ERA5). We examine the long-term changes in precipitation characteristics in different data sources over Europe. We calculate differences between the datasets and attempt to identify causes of these differences and the specific behavior of the datasets. In addition to the analysis of the trend of the seasonal totals and the probability of precipitation, we focus also on the trends of a day-to-day variability of precipitation. We consider wet-to-wet and dry-to-dry transition probabilities as a measure of short-term precipitation variability. Long-term trends of seasonal values of precipitation variables and their statistical significance are calculated by non-parametric methods (Mann-Kendall test, Kendall statistic). The analysis is conducted on a seasonal basis, with emphasis on winter and summer. We find that each of the datasets has its advantages and drawbacks. Trends in the reanalyses deviate considerably from the observed datasets. This is mainly because of the changes in the type and amount of assimilated data over time. The weakness of the gridded datasets is the unstable number of stations entering the interpolation in time. The main disadvantage of the station data is the lack of representativeness of some climate stations.