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In Hungary, the regular precipitation measurements began in the 1850s under the direction of the then Austrian Meteorological Institute based in Vienna, and from 1870 onwards continued under the Budapest-based "Meteorológiai és Földdelejességi Magyar Királyi Központi Intézet", now HungaroMet Hungarian Meteorological Service. Over the decades, the measurements have undergone many changes, including changes in instrumentation and relocation of stations, which cause inhomogeneities in the data series. In addition, the number of stations and the density of the station network have also changed significantly. As a result, the data series need to be homogenized and interpolated to a uniform grid in order to study the climate and its changes over the long term. In this paper, we present the methods used, discuss the station systems used for precipitation homogenization and interpolation in different periods, analyze the main verification statistics of homogenization and also the results of interpolation, and examine the annual, seasonal, and monthly precipitation data series and their extremes for the period 1854–2022. [ABSTRACT FROM AUTHOR]

The article presents a recent update of a comprehensive dataset of long‐term series of precipitation data from instrumental observations in the Greater Alpine Region (GAR), that is, the region of Europe including the Alpine mountain range and their nearer surroundings (4°–19° E in longitude and 43°–49° N in latitude). A comparison to different national homogenized datasets is also presented. Results show that in the national homogenized datasets more breaks have been detected due to higher station density. They also demonstrate the necessity of constant exchange with data providers. The resulting trends in all datasets are mainly weak and only a minority of them is statistically significant. In most cases the similarity of statistical index numbers are promising, with, for example, small RMSE between the presented new HISTALP homogenization and the time series of the national homogenized datasets. Nevertheless, for some stations higher differences occur and break signals are not what would be expected due to possible causes in the station history. The differences between the national and the HISTALP new homogenization—due to, for example, different methods used, different points in time when the homogenization took place, different options of data handling (combination of station data, gap filling routines, ...) and different reference stations—illustrate the inherent uncertainty unavoidably associated to homogenization and point out the need of careful communication and use of the data. On the other hand, the results highlight the advantage of consistently homogenized datasets, versus the risks associated with mixing results from different homogenizations. [ABSTRACT FROM AUTHOR]

Gridded analyses of observed precipitation are an important data resource for environmental modelling, climate model evaluation and climate monitoring. In Europe, datasets that resolve the rich mesoscale variations widely exist for the national territories, but similar datasets covering the entire continent are more recent. Here, we evaluate daily precipitation in two newly available pan‐European datasets: E‐OBS (v19.0e), a statistical analysis from rain‐gauge data, and ERA5, the new global reanalysis from ECMWF. Special interest is on how the refinements of grid spacing, the methodological upgrades and the quantification of uncertainty (ensemble), bear on capabilities at the mesoscale. The evaluation is conducted in three subregions, the Alps, the Carpathians and Fennoscandia, and involves as reference high‐quality regional datasets derived from dense rain‐gauge data. The study suggests that E‐OBS and ERA5 agree qualitatively well with the reference datasets. Major mesoscale patterns in the climatology (mean, wet‐day frequency, 95% quantile) are reproduced. The improvement over earlier versions of the datasets is evident. ERA5 was found to overestimate mean precipitation in all regions, related to too many wet days. The accuracy of E‐OBS was found to depend on station density, with spatial and temporal variations clearly less accurate in data sparse regions. In comparison, E‐OBS turned out to be superior to ERA5 in regions with dense data, but the two datasets are on a par in regions with sparse data, and partly ERA5 has advantages. For both datasets we find that the spatial resolution is coarser than the grid spacing, with overly smooth fields and an underestimation of high quantiles. Also, both datasets were found to be clearly overconfident in their uncertainty characterization (too small ensemble spread). Overall, the two datasets advance the characterization of precipitation on a pan‐European scale, but users are advised to take residual limitations into account in applications. [ABSTRACT FROM AUTHOR]