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Abstract. In this paper we present EMO-5 (“European Meteorological Observations”, spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020).

Abstract. In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

<p>Hydrological extremes are non-stationary, displaying long-term trends and natural oscillations. These changes in extremes can be driven by multiple factors including climatic (climate variability, climate change) and socio-economic (land use changes, water management changes) factors. In this work, we analyse extreme river flows in Europe for the period 1950-2020. We aim to identify long-term trends in extreme floods and estimate the contribution of the two aforementioned factors in these trends. The assessment is performed with modelled streamflow data generated with the spatially distributed physically based model LISFLOOD. We force the model with bias-corrected and statistically downscaled climate data derived from the ERA5-Land climate reanalysis and the EMO-5 dataset. We also created a variant of the climate dataset with the global warming effect removed statistically from the data. Return periods of extreme flood events are estimated through a non-stationary extreme value analysis for each river point with an upstream area greater than 100 km<sup>2</sup>. To disentangle the influence of the different factors driving changes in extreme flows, the hydrological model is run under various scenarios: (i) historical (historical climate and dynamic socio-economic) (ii) static society (historical climate and static socio-economic), (iii) counterfactual climate (historical climate without global warming and dynamic socio-economic). Available preliminary results enable presenting long-term space-time dynamics of European floods.</p>