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

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 1-h precipitation accumulation in millimeters. For each grid cell, the availability for 1-h accumulations data is either no data or full availability, and can be determined from "dataset1/data1" through the "nodata" value (-9999000.0).

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 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 56 days of rainfall during this period, the most of any country.

ERA5-Land daily: Total precipitation, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

Source data:
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to:
- aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum)
- while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/).
For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land.
The steps included aggregation and enhancement, specifically:
1. spatially aggregate CHELSA to the resolution of ERA5-Land
2. calculate proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

Data available is the daily sum of precipitation.
File naming:
era5_land_daily_prectot_YYYYMMDD_sum_30sec.tif
e.g.:era5_land_daily_prectot_20200418_sum_30sec.tif

The date within the filename is Year, Month and Day of timestamp.

Pixel values:
mm * 10
Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18:00:00N
west: 32:00:30W
east: 70:00:00E

Temporal extent:
01.01.2000 - 31.12.2020
NOTE: Due to file size, only 2020 data are available here. Data for other years are available on request.

Spatial resolution:
30 arc seconds (approx. 1000 m)

Temporal resolution:
daily

Lineage:
Dataset has been processed from Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Format: GeoTIFF

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Representation type: Grid

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

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).

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

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

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

This historical weather dataset provides hourly weather data for a number of major European Cities between 2003-01-01 and 2022-12-31. You can use this data to analyze and understand how weather has impacted your business, enrich your website with weather-related information, or enhance your data science projects with weather data. In addition to standard weather measurements such as air pressure, temperature, precipitation, and wind speed, this dataset includes solar radiation and UV index data as well. The full list of fields is provided in the documentation.

Key features:

• Hourly historical weather data between 2003 and 2022
• Includes solar radiation data
• Provides information on standard weather measurements such as air pressure, temperature, precipitation, and wind speed
• Offers insights into how weather has impacted your business

This Historical Weather Data is crucial for businesses needing detailed Climate Data, including Precipitation Data and Wind Data, to make informed decisions

Generated using Copernicus Climate Change Service information 2023 Contains modified Copernicus Climate Change Service information 2023

Past, present and future rainfall erosivity in Northwestern Europe calculated from convection-permitting climate simulations in CNRM-AROME (Lucas-Picher et al., 2022; https://doi.org/10.1007/s00382-022-06637-y) using emission scenario RCP 8.5. A description of the methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (https://doi.org/10.5194/hess-28-87-2024).

The datafile contains gridded data on a 0.25 degree regular grid of daily precipitation sums in mm.

We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu)

According to a survey carried out in 2024, almost three-quarters of Spaniards stated that they had experienced worse-than-usual extreme weather events. Of the European countries considered, Italy was the second one where more than half of the respondents also agreed that they have been feeling worse-than-usual events, like droughts, floods, storms, and extreme temperatures.

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.

This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.

The data contains observations measured with weather surveys of the Finnish Meteorological Institute. The following quantities are available as a composite image containing all weather radars: radar reflectivity factor, rain intensity and one hour and day rainfall accumulation. A radar reflectivity factor, rain intensity and wind radial velocity are available as images of individual weather radars.

Regardless of whether the rain in Spain stays mainly in the plain, the truth is annual precipitations in the Mediterranean country experienced a downward trend in recent years, with around 536 millimeters of rainfall recorded in 2023. For instance, March – one of Spain's wettest months – registered just over 21 millimeters of rain in 2023, down from a record high of 163 millimeters in March 2018. Spain: Europe’s suntrapMany picture Spain as a dream summer holiday destination – Mediterranean cuisine in the form of tapas, great beaches, and what many visit the country for – its warm climate and sweet sunshine. This enthusiasm for the European country is then not too surprising, since most of its sunniest areas exceeded 3,000 hours of sunshine according to data provided by the Spanish Statistics Institute. Tourism constitutes an essential industry for the Spanish economic systemTravel and tourism have become one of the leading engines of growth for the Spanish economy, featuring an ongoing increase in the GDP contribution over the last years – despite a drop due to the COVID-19 pandemic – and is projected to reach nearly 165 billion euros in 2023.

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes. The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets. The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).

In Europe (EEA 33), the total value of economic losses caused by weather and climate-related events between 1980 and 2017 amounted to approximately 453 billion euros. The annual average economic loss increased through the decades from an average of 7.4 billion (1980-1989) to 14 billion euros (2000-2009). The most expensive extreme weather event to hit Europe economically was the 2002 flood in Central Europe, amounting to 21 billion euros.

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