The average for 2020 based on 39 countries was 829 mm per year. The highest value was in Iceland: 1940 mm per year and the lowest value was in Moldova: 450 mm per year. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

This dataset provides values for PRECIPITATION AMOUNT OF PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This statistic presents the number of days with precipitation in leading European countries between January 1 and March 31, 2018. According to data provided by LAL, the Netherlands had ** days of rainfall during this period, the most of any country.

The European climatological gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub-)daily precipitation product covering 8000000 square kilometers of Europe at high spatial resolution. It consists of 1-h and 24-h precipitation accumulations (every clock-hour) at a 2-km grid for the period 2013 through 2022. It is based on the European Meteorological Network (EUMETNET) Operational Program on the Exchange of weather RAdar Information (OPERA) gridded radar dataset of 15-min instantaneous surface rain rates. For EURADCLIM, first methods have been applied to further remove non-meteorological echoes from these images by applying two statistical methods and a satellite-based cloud type mask. Second, the radar composites are merged with the rain gauge data from the European Climate Assessment & Dataset (ECA&D) in order to substantially improve its quality. We expect to rerun EURADCLIM once a year over the entire period, using all available ECA&D rain gauge data, and extend it with one year of data. This will result in a new version of this dataset. Project EURADCLIM was financed by KNMI’s multi-annual strategic research programme (project number 2017.02). The EURADCLIM dataset is based on the OPERA surface radar rain rate and daily precipitation sums of the rain gauge networks provided by the European national weather services and other data holding institutes, through ECA&D. With respect to version 1, the changes include slightly improved removal of non-meteorological echoes, somewhat better rain gauge coverage over the years 2013 to 2020, and years 2021 and 2022 have been added to the dataset. Usage: For each month a zip file is provided. The data are in UTC, where the time in the unzipped filenames is the end time of observation in UTC. Object "dataset1/data1" contains the 24-h precipitation accumulation in millimeters. For a given grid cell, it is equal to the "nodata" value (-9999000.0) in case data availability of the underlying 1-h accumulations is < 83.3%. Object "dataset2/dataset1" contains the number of valid values for each radar grid cell (count), i.e., the number of underlying 1-h accumulations that have been used.

The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys. This dataset makes use of precipitation data available in the Climate Data Store (i.e. E-OBS gridded land-only observational dataset and ERA5 reanalysis) combined with additional datasets capable of improving the spatial and temporal resolution of the precipitation data, making it suitable for pluvial flood analysis at city scales. These are derived from i) the network of meteorological stations included in the European Climate Assessment & Dataset (ECA&D) programme and ii) dynamically downscaled ERA5 reanalysis at 2 km x 2 km (ERA5-2km) using the regional climate model COSMO-CLM and accounting for urban parameterization, specifically performed for the 20 European cities identified as vulnerable to urban pluvial flooding. At the European scale, E-OBS and ERA5 precipitation data are used to compute indicators at different temporal resolutions (i.e. daily, monthly, yearly, and 30-year) according to the type of indicator. The precipitation amounts at fixed return periods are also computed for point observations from meteorological stations using the ECA&D network and are then interpolated onto the E-OBS grid. At the city scale, a dynamically downscaled ERA5-2km precipitation data are instead used to derive daily indicators, allowing city stakeholders to detect and rank local extreme precipitation events and evaluate their magnitude. This dataset was produced on behalf of the Copernicus Climate Change Service.

This dataset provides monthly total precipitation amounts (in millimeters) recorded across Europe. Each row represents the total precipitation for one month and year, facilitating analyses of hydrological cycles, droughts, flooding risks, and long-term climate variability. The data can be used for time series analysis, climate modeling, and environmental studies focused on European precipitation patterns over multiple decades.

The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods.

Bias-adjusted daily time series of mean, minimum (Tn) and maximum (Tx) temperature, and precipitation (Pr) for the period 1981–2100 for an ensemble of Regional Climate Models (RCMs) from EURO-CORDEX. RCMs are used to downscale the results of Global Climate Models from the Coupled Model Intercomparison Project Phase 5. All RCMs are run over the same numerical domain covering the European continent at a resolution of 0.11°. Historical runs, forced by observed natural and anthropogenic atmospheric composition, cover the period from 1950 to 2005; the projections (2006–2100) are forced by two Representative Concentration Pathways (RCP), namely, RCP4.5 and RCP8.5. RCMs’ outputs have been bias-adjusted using the methodology described in e.g. Dosio and Paruolo (2011) using the observational data set EOBSv10, and applied to the EURO-CORDEX data by Dosio (2016) and Dosio and Fischer (2018)

For further information the readers are referred to the following publications: Dosio, A., Fischer, E. M. (2018). Will Half a Degree Make a Difference? Robust Projections of Indices of Mean and Extreme Climate in Europe Under 1.5°C, 2°C, and 3°C Global Warming. Geophysical Research Letters, 45(2), 935–944. https://doi.org/10.1002/2017GL076222 Dosio, A. (2016). Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. Journal of Geophysical Research: Atmospheres, 121(10), 5488–5511. https://doi.org/10.1002/2015JD024411 Dosio, A., Paruolo, P. (2011). Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. Journal of Geophysical Research, 116(D16), 1–22. https://doi.org/10.1029/2011JD015934

Metadata: Title: Rainfall erosivity in Europe Description: This map provides a complete rainfall erosivity dataset for European Union (28 member States) and Switzerland based on REDES database with high temporal resolution rainfall measurements of 26,394 years. Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map. REDES is provided as a point database including R-factor for each of the 1,675 stations (see below). Monthly R-factor maps are also available (see below) R-factor detailed assessments for Greece and Switzerland are available (see below). Future projections (2050) of R-factor are available (see below). Spatial coverage: European Union (28 Countries) & Switzerland Pixel size: 500m Measurement Unit: MJ mm ha-1 h-1 yr-1 Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage: 40 years - Predominant in the last decade: 2000 - 2010 Users can downloads Raw data, Baseline map (2010), Monthly erosivity, Future projections (2050), Past erosivity (1961-2000). Note: We also make available the Global Rainfall Erosivity (GloREDa) database R-factor in Europe The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,675 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. Info: Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Begueria, S., Alewell, C. 2015. Rainfall erosivity in Europe. Sci Total Environ. 511: 801-814. REDES: Rainfall Erosivity Database on the European Scale The Rainfall Erosivity Database on the European Scale (REDES) includes high temporal resolution precipitation data and the claculated R-factor from 1,675 precipitation stations within the European Union (EU) and Switzerland. The Rainfall Erosivity Database on European Scale (REDES) of precipitation stations is the result of calculating the R-factor for a total of 26,394 years with a mean value of 17.1 years per station. The data collection exercise of high temporal resolution data began in March 2013 and was concluded in May 2014. Data for additional 134 stations were collected in 2015. For the present rainfall erosivity data collection exercise, a participatory approach has been followed in order to collect data from all Member States (Aknowledgments). The precipitation data collected from the 28 countries across Europe have different temporal resolutions: 60-min, 30-min, 15-min, 10-min and 5-min. In order to homogenise the R-factor results calculated using different time-step data, conversion factors were established to have the data at the 30-min temporal resolution (reference scale). Info: Panagos et al., 2015; Borrelli et al., 2016; Panagos et al., 2016; Ballabio et al., 2017; Meuburger et al., 2012 Future Erosivity projections in 2050 based on climate change The rainfall erosivity in 2050 was modelled based on on a moderate climate change scenario (HadGEM RCP 4.5) and using as main data sources the REDES based European R-factors and as covariates the WorldClim climatic datasets. Although the rainfall erosivity projections are based on many uncertainties, this pan-European spatial estimation highlights the areas where rainfall erosivity is projected to undergo substantial changes. The predicted mean increase in R-factor is expected also to increase the threat of soil erosion in Europe. However, climate change might substantially affect land cover and land use, which might counterbalance or enhance some erosional trends. The most prominent increases of R-factors are predicted for North-Central Europe, the English Channel, The Netherlands and Northern France. On the contrary, parts of the Mediterranean basin show a decrease of rainfall erosivity. he mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha-1 h-1yr-1 till 2050 showing a relative increase of 18% compared to baseline data (2010).The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models. Info: Panagos, P., Ballabio, C., Meusburger, K., Spinoni, J., Alewell, C., Borrelli, P. 2017. Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets. Journal of Hydrology, 548: 251-262. Monthly Rainfall Erosivity in Europe The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha-1 h-1) compared to winter (87 MJ mm ha-1 h-1). The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sumof all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areaswith highest risk of soil losswhere conservationmeasures should be applied in different seasons of the year. Info: Ballabio, C., Borrelli, P. , Spinoni, J., Meusburger, K., Michaelides, S., Begueria, S., Klik, A., Petan, S., Janecek, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Tadić, M.P., Nazzareno, D., Kostalova, J., Rousseva, S., Banasik, K., L., Alewell, C. , Panagos, P. 2017. Mapping monthly rainfall erosivity in Europe. Sci Total Environ. 579: 1298-1315 Past Erosivity and trend detection (1961-2018) In this study we reconstructed past rainfall erosivity in Europe for the period 1961–2018, with the aim to investigate temporal changes in rainfall erosivity. As input data, we used the Rainfall Erosivity Database at European Scale (REDES) and Uncertainties in Ensembles of Regional Reanalyses (UERRA) rainfall data. Using a set of regression models, which we derived with the application of the k-fold cross-validation approach, we computed the annual rainfall erosivity for the 1675 stations forming the REDES database. Based on the reconstructed data, we derived a rainfall erosivity trend map for Europe where the results were qualitatively validated. Among the stations showing a statistically significant trend, we observed a tendency towards more positive (15%) than negative trends (7%). In addition, we also observed an increasing tendency of the frequency of years with maximum erosivity values. Geographically, large parts of regions such as Eastern Europe, Scandinavia, Baltic countries, Great Britain and Ireland, part of the Balkan Peninsula, most of Italy, Benelux countries, northern part of Germany, part of France, among others, are characterized by a positive trend in rainfall erosivity. By contrast, negative trends in annual rainfall erosivity could be observed for most of the Iberian Peninsula, part of France, most of the Alpine area, Southern Germany, and part of the Balkan Peninsula, among others. The new dataset of rainfall erosivity trends reported in this study scientifically provides new information to better understand the impacts of the ongoing erosivity trends on soil erosion across Europe, while, from a policy perspective, the gained findings provide new knowledge to support the development of soil erosion indicators aiming at promoting mitigation measures at regional and pan-European level. Info: Bezak, N., Ballabio, C., Mikoš, M., Petan, S., Borrelli, P., Panagos, P. 2020. Reconstruction of past rainfall

NetCDF files containing estimated occurence frequency of the present day 10-year return levels of precipitation for different time periods and scenarios in the future climate. Additionally includes 10-year return levels of 3-hourly, 24 -hourly, 48-hourly and 72-hourly precipitation amounts for the simulated present day climate. The fields are multi model means of regional climate model simulations (EURO-CORDEX).

2 km resolution data from the NIMROD system data describe rain-rate observations recorded by the Jersery rain radar, Channel Islands, by NIMROD, which is a very short range forecasting system used by the Met Office. 2 km rain rate data are available from 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals.

Data from the NIMROD system data describe rain-rate observations in Northern Europe taken by NIMROD, which is a very short range forecasting system used by the Met Office. Composite European data are available from April 2002 until present, collected by a network of rain radars at northern European stations. Radar images from the 15 C-band (5.3 cm wavelength) radars around Europe at 5 km resolution, are received by the Nimrod system at 15 minute intervals. Data products are available since April 2002, whilst image products are available from February 2003. Each file has been compressed and then stored within daily tar archive files.

The precipitation rate analysis uses processed radar and satellite data, together with surface reports and Numerical Weather Prediction (NWP) fields. Europe has a network of 15 C-band rainfall radars and data form these are processed by the Met Office NIMROD system. The data files contain integer precipitation rates in unit of (mm/hr)*32. Each value is between 0 and 32767. In practice it is rare to see a value in excess of 4096 i.e. 128 mm/hr

CEDA are not able to fulfil requests for data that are missing from this archive. The data may be available at a cost by contacting the Met Office directly with required dates. It is worth contacting CEDA first to check if the reason for the gap is already identified as being due to the data not existing at all.

Daily and monthly rainfall records for our station at Coon in Co. Kilkenny. This station is now closed.

Rainfall accumulation (mm) HDF5 European Radar composites Hourly accumulations 2 km x 2 km grid 15 minute updates at DT+15 minutes

Quality: Odyssey will generate and archive composite products from raw single site radar data using common pre-processing and compositing algorithms. Expected performance : - The target availability of composite products produced, delivered and archived will be an average of 99.0%. - Composite products produced within 15 minutes of data time on at least 95% of occasions. - Composite products delivered(1) within 20 minutes of data time on at least 95% of occasions. - Normally, there would be no downtime during maintenance slots and planned switching of nodes - Where available, gaps in the archive hosted in Météo France will be populated with data archived in Exeter within 7 working days of notification. Performance Measure: - Performance of composite availability and timeliness will be measured at the Odyssey system. - Production timeliness will be recorded as the completion time of composite generation at Odyssey. - Availability and timeliness will be measured monthly. Fault resolution: - For system or hardware faults that affect availability, the target will be to respond and fix the fault within 2 hours of notification on 98% of occasions. Contingency: - The Odyssey service will be maintained despite IT infrastructure failures at one of the Odyssey nodes. - Contingency will be provided by the back-up Odyssey node. Switching of operational status between Odyssey nodes will occur within 30 minutes of outage. - The main Odyssey archive will be hosted at Météo France and a backup hosted at the Met Office Support Cover: - The ability to switch operational Odyssey node will be provided 24 hours a day (24/7/365) - Other support activities will take place during Normal Working Hours (of the responsible member) Service Failures: A tolerable level of service failure would be: - one ‘break of up to 15 minutes in any 7 day period - one ‘break’ of up to 60 minutes in any quarter of a year - one ‘break’ over 60 minutes in any one year, with service being restored within 4 hours. A ‘break’ denotes a reduction in service delivery, however the service will be deemed to be met if the agreed alternative output is being supplied. Service description: - Instantaneous Surface Rain rate - Domain – Whole of Europe - Projection – Lambert Equal Area - Update frequency – 15 minutes - Issue time – Approximately 15 minutes after data time - Delivery method – FTP to NM(H)S via GTS/RMDCN or Internet - Available in real-time and via archive - Format: HDF5 (structure is compliant with the Eumetnet ODIM specification) Permitted use: To be confirmed Fault reporting: Reported to the UK Met Office Weather Desk. (1) Applies only to NMS receiving composite products by direct RMDCN connections

The average for 2020 based on 27 countries was 756 mm per year. The highest value was in Slovenia: 1162 mm per year and the lowest value was in Cyprus: 498 mm per year. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

Ground-based weather radars provide precipitation estimates with wide coverage and high spatiotemporal resolution, but usually need adjustment with rain gauge data to obtain a reasonable accuracy. The (near) real-time availability and density of rain gauge networks operated by official institutes, especially national meteorological and hydrological services, is often relatively low. Crowdsourced rain gauge networks typically have a much higher density than networks from official institutes. Data from PWSs from brand Netatmo were obtained. Here, pan-European 1-h radar precipitation accumulations have been adjusted with 1-h rain gauge accumulations from personal weather stations (PWSs) for each clock-hour. The radar data were obtained from the Operational Program on the Exchange of weather RAdar information (OPERA) over the period 1 September 2019–31 August 31 2020. Two statistical methods and a satellite cloud type mask have been applied to the OPERA data to further remove non-meteorological echoes. Although not all these methods could be applied in (near) real-time, the OPERA dataset is representative of near (real-time) data, because these methods do only concern non-meteorological echo removal and not precipitation estimation itself. The Netatmo PWS data were subjected to quality control employing neighbouring PWSs and unadjusted radar data, before they were merged with the radar accumulations. A spatial adjustment (merging) method has been employed. The dataset covers 78% of geographical Europe. The dataset aims to show the potential of crowdsourced rain gauge data to improve radar data in (near) real-time.

Daily and monthly rainfall records for our station at Foxford G.S. in Co. Mayo. This station is now closed.

Daily and monthly rainfall records for our station at Derriana in Co. Kerry.

Daily and monthly rainfall records for our station at Aherlamore in Co. Cork.

Daily and monthly rainfall records for our station at Virginia in Co. Cavan.