100+ datasets found
  1. European average temperature relative to pre-industrial period 1850-2019

    • statista.com
    Updated Apr 19, 2023
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    Statista (2023). European average temperature relative to pre-industrial period 1850-2019 [Dataset]. https://www.statista.com/statistics/888098/europes-annual-temperature-anomaly/
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    Dataset updated
    Apr 19, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    Europe's average temperature has increased significantly when compared with the pre-industrial period, with the average temperature in 2014 2.22 degrees Celsius higher than average pre-industrial temperatures, the most of any year between 1850 and 2019.

  2. European Monthly Average Temperature Dataset (TG Variable)

    • figshare.com
    csv
    Updated Jul 3, 2025
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    Duane Ebesu (2025). European Monthly Average Temperature Dataset (TG Variable) [Dataset]. http://doi.org/10.6084/m9.figshare.29470154.v1
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    csvAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Duane Ebesu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset provides monthly average values of the TG variable, representing mean air temperature across European regions. It spans multiple years, supporting analysis of seasonal and interannual temperature variability. The data are suitable for climate research, trend detection, modeling efforts, and understanding temperature-related environmental impacts across Europe. Structured for compatibility with other Copernicus climate datasets, it can be integrated with variables such as precipitation, cloud cover, and wind speed to examine broader climate patterns.

  3. Average temperature increase in capital cities in the European Union 2050

    • statista.com
    Updated Dec 20, 2023
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    Statista (2023). Average temperature increase in capital cities in the European Union 2050 [Dataset]. https://www.statista.com/statistics/1026668/annual-temperature-increase-cities-in-european-union/
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    Dataset updated
    Dec 20, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Europe
    Description

    Due to climate change the Slovenian capital Ljubljana is expected to see its mean annual temperature increase by 3.5 degrees Celsius by 2050. This is the largest increase throughout the European Union, and will be comparable to current temperatures recorded in Virginia Beach, USA. Northern European cities such as London, Paris and Berlin will see temperatures rise to levels currently experienced in the Australian cities of Canberra and Melbourne.

  4. Temperature statistics for Europe derived from climate projections

    • cds.climate.copernicus.eu
    netcdf
    Updated Jan 31, 2025
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    ECMWF (2025). Temperature statistics for Europe derived from climate projections [Dataset]. http://doi.org/10.24381/cds.8be2c014
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    netcdfAvailable download formats
    Dataset updated
    Jan 31, 2025
    Dataset provided by
    European Centre for Medium-Range Weather Forecastshttp://ecmwf.int/
    Authors
    ECMWF
    License

    https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/cc-by/cc-by_f24dc630aa52ab8c52a0ac85c03bc35e0abc850b4d7453bdc083535b41d5a5c3.pdfhttps://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/cc-by/cc-by_f24dc630aa52ab8c52a0ac85c03bc35e0abc850b4d7453bdc083535b41d5a5c3.pdf

    Time period covered
    Jan 1, 1986 - Dec 31, 2085
    Area covered
    Europe
    Description

    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.

  5. European Monthly Average Minimum Temperature Dataset (TN Variable)

    • figshare.com
    csv
    Updated Jul 3, 2025
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    Duane Ebesu (2025). European Monthly Average Minimum Temperature Dataset (TN Variable) [Dataset]. http://doi.org/10.6084/m9.figshare.29470379.v1
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    csvAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    figshare
    Authors
    Duane Ebesu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains monthly averages of the TN variable, representing minimum daily air temperatures across European regions. It spans several decades, enabling analysis of seasonal trends, cold extremes, and long-term shifts in minimum temperatures. The data are essential for climate studies, risk assessments related to frost or cold events, and integration into broader climate models. Harmonized with other Copernicus datasets, it can be combined with temperature maxima, precipitation, and additional climate indicators to study environmental change and variability across Europe.

  6. Perceptions on the hottest annual global temperatures in the last 18 years...

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Perceptions on the hottest annual global temperatures in the last 18 years in Europe [Dataset]. https://www.statista.com/statistics/952827/perceptions-on-climate-change-in-europe/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 28, 2018 - Oct 16, 2018
    Area covered
    Europe
    Description

    This statistic presents the perceived changes in annual global temperatures in the last 18 years, in selected European Countries in 2018. According to data published by Ipsos, the average guess among respondents in these countries was between 7 to 13 years, compared to the actual figure of **.

  7. O

    Weather Data

    • data.open-power-system-data.org
    csv, sqlite
    Updated Sep 16, 2020
    + more versions
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    Stefan Pfenninger; Iain Staffell (2020). Weather Data [Dataset]. http://doi.org/10.25832/weather_data/2020-09-16
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    csv, sqliteAvailable download formats
    Dataset updated
    Sep 16, 2020
    Dataset provided by
    Open Power System Data
    Authors
    Stefan Pfenninger; Iain Staffell
    Time period covered
    Jan 1, 1980 - Dec 31, 2019
    Variables measured
    utc_timestamp, AT_temperature, BE_temperature, BG_temperature, CH_temperature, CZ_temperature, DE_temperature, DK_temperature, EE_temperature, ES_temperature, and 75 more
    Description

    Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

  8. Temperature - daily updated gridded fields for daily mean temperature...

    • dataplatform.knmi.nl
    Updated Aug 8, 2024
    + more versions
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    knmi.nl (2024). Temperature - daily updated gridded fields for daily mean temperature derived from stations observations in Europe (E-OBS dataset) [Dataset]. https://dataplatform.knmi.nl/dataset/daily-updated-tg-eobs-1
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    Dataset updated
    Aug 8, 2024
    Dataset provided by
    Royal Netherlands Meteorological Institutehttp://www.knmi.nl/
    Area covered
    Europe
    Description

    The E-OBS dataset (https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php) consists of gridded fields created from station series throughout Europe. The dataset contains preliminary daily updates of the E-OBS dataset for daily mean temperature. Only the last 60 days are saved in this dataset, so the latest month is completely available at all times after the monthly update. This dataset is currently unavailable on our platform. We are actively working to resolve this issue, but we do not have a definitive timeline for when the download functionality for this dataset will be restored. In the meantime, you can access the dataset directly from the original source using the following alternative link: https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php.

  9. Average monthly temperature Germany 2024-2025

    • statista.com
    Updated Jan 31, 2025
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    Statista (2025). Average monthly temperature Germany 2024-2025 [Dataset]. https://www.statista.com/statistics/982472/average-monthly-temperature-germany/
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    Dataset updated
    Jan 31, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2024 - Jan 2025
    Area covered
    Germany
    Description

    Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

  10. T

    TEMPERATURE by Country in EUROPE/1000

    • tradingeconomics.com
    csv, excel, json, xml
    + more versions
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    TRADING ECONOMICS, TEMPERATURE by Country in EUROPE/1000 [Dataset]. https://tradingeconomics.com/country-list/temperature?continent=europe/1000
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    json, excel, xml, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Area covered
    Europe
    Description

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

  11. E

    Data from: Europe Annual Temperature 1950-2009

    • dtechtive.com
    • find.data.gov.scot
    xml, zip
    Updated Feb 21, 2017
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    Lancaster University (2017). Europe Annual Temperature 1950-2009 [Dataset]. http://doi.org/10.7488/ds/1801
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    xml(0.0037 MB), zip(6.719 MB)Available download formats
    Dataset updated
    Feb 21, 2017
    Dataset provided by
    Lancaster University
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    Europe
    Description

    Annual mean temperature data for the period 1950 to 2009 for Europe. Data is gridded at a cell size of 0.25 degrees. E-OBS daily data downloaded from http://eca.knmi.nl/download/ensembles/download.php#datafiles in NetCDF format. Data converted to Arc GRID and annual averages calculated for each year, using map algebra. Please see http://eca.knmi.nl/download/ensembles/download.php#datafiles for terms and conditions of use. Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-14 and migrated to Edinburgh DataShare on 2017-02-21.

  12. e

    Pan-European Annual Thermal Amplitude

    • metadata.europe-geology.eu
    Updated Jul 28, 2021
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    Technical University of Cartagena (UPCT) (2021). Pan-European Annual Thermal Amplitude [Dataset]. https://metadata.europe-geology.eu/record/basic/61012f47-8b28-45e0-8103-7ee40a010833
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    Dataset updated
    Jul 28, 2021
    Dataset authored and provided by
    Technical University of Cartagena (UPCT)
    License

    http://inspire.ec.europa.eu/metadata-codelist/ConditionsApplyingToAccessAndUse/noConditionsApplyhttp://inspire.ec.europa.eu/metadata-codelist/ConditionsApplyingToAccessAndUse/noConditionsApply

    Area covered
    Description

    Difference between the maximum and minimum annual average temperature

  13. NOAA/WDS Paleoclimatology - European Mean and Spatial Summer Temperature...

    • catalog.data.gov
    Updated Mar 1, 2025
    + more versions
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    (Point of Contact); NOAA World Data Service for Paleoclimatology (Point of Contact) (2025). NOAA/WDS Paleoclimatology - European Mean and Spatial Summer Temperature Reconstructions Since Roman Times [Dataset]. https://catalog.data.gov/dataset/noaa-wds-paleoclimatology-european-mean-and-spatial-summer-temperature-reconstructions-since-ro1
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    Dataset updated
    Mar 1, 2025
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Description

    This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|historical|tree ring with a geographic location of Europe. The time period coverage is from 2088 to -53 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

  14. European temperature anomalies in November 1979-2023

    • statista.com
    Updated Nov 15, 2023
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    Statista (2023). European temperature anomalies in November 1979-2023 [Dataset]. https://www.statista.com/statistics/1445543/european-surface-air-temperature-anomaly-november/
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    Dataset updated
    Nov 15, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Europe
    Description

    In November 2023, the surface air temperature anomaly in Europe was 0.48°C, relative to the 1991-2020 average for that month. This was 1.26°C colder than November 2015, which was Europe's hottest November on record, with a temperature anomaly surpassing 1.74°C.

  15. EU High Resolution Temperature and Precipitation

    • data.europa.eu
    netcdf
    Updated Apr 4, 2018
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    Joint Research Centre (2018). EU High Resolution Temperature and Precipitation [Dataset]. https://data.europa.eu/data/datasets/jrc-liscoast-10011?locale=en
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    netcdfAvailable download formats
    Dataset updated
    Apr 4, 2018
    Dataset authored and provided by
    Joint Research Centrehttps://joint-research-centre.ec.europa.eu/index_en
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Area covered
    European Union
    Description

    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

  16. Past and future weather extremes across Europe

    • zenodo.org
    • data.niaid.nih.gov
    zip
    Updated Dec 20, 2022
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    Tobias Seydewitz; Tobias Seydewitz (2022). Past and future weather extremes across Europe [Dataset]. http://doi.org/10.5281/zenodo.7463485
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    zipAvailable download formats
    Dataset updated
    Dec 20, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tobias Seydewitz; Tobias Seydewitz
    License

    https://opensource.org/licenses/BSD-2-Clausehttps://opensource.org/licenses/BSD-2-Clause

    Area covered
    Europe
    Description
    Past and future weather extremes across Europe
    
    This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [Zhang et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the ERA5-Land reanalysis dataset, and for future projections, we use modelled daily weather records from EURO-CORDEX [Christensen et al., 2020, Muñoz, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from ERA5 and EURO-CORDEX, respectively [Giannakopoulos et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while.
    
    Parameters of the analysis of the percentile-based extreme.
    TypeVariablePercentileWindowMin durationRescalingMonthsBootstrapping
    Heat wavetmin and tmax9053max6, 7, 8yes
    Cold wavetmin and tmax1053max1, 2, 10, 11, 12yes
    Flash droughtswvl 0-28cm3055mean6, 7, 8, 9, 10no
    Fire riskFWI9051mean3, 4, 5, 6, 7, 8, 9yes
    Xuebin Zhang, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1.
    
    Samuel Jonson Sutanto, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276.
    
    J. Sabater Muñoz. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019.
    
    O. B. Christensen, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf
    
    Barry J. F. Biggs, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677.
    
    Giannakopoulos, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7
    
    Funding
    Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.
    
  17. Sea surface temperature anomalies in Europe Seas, Jan. 2020

    • sextant.ifremer.fr
    • sdi.eea.europa.eu
    • +2more
    esri:rest, ogc:wms +1
    Updated Jan 27, 2020
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    European Environment Agency (2020). Sea surface temperature anomalies in Europe Seas, Jan. 2020 [Dataset]. https://sextant.ifremer.fr/record/789de867-c056-4f59-ac42-c7bbf71e662b/
    Explore at:
    www:url, esri:rest, ogc:wmsAvailable download formats
    Dataset updated
    Jan 27, 2020
    Dataset authored and provided by
    European Environment Agencyhttp://www.eea.europa.eu/
    License

    http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitationshttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Time period covered
    Jan 1, 1989 - Dec 31, 2013
    Area covered
    Description

    This raster dataset represents the Sea Surface Temperature (SST) anomalies, i.e. changes of sea temperatures, in the European Seas.

    The dataset is based on the map "Mean annual sea surface temperature trend in European seas" by Istituto Nazionale di Geofisica e Vulcanologia (INGV), which depicts the linear trend in sea surface temperature (in °C/yr) for the European seas over the past 25 years (1989-2013).

    Since all changes of sea temperatures can be considered to have an impact on the marine environment, the pressure layer includes absolute values of SST anomalies, i.e. negative/decreasing temperature trends were changed to positive values so that they represent a pressure. The original data was in a 1° grid format but was converted to a 100 km resolution, adapted to the EEA 10 km grid and clipped with the area of interest.

    This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

  18. W

    Climate data De Bilt; temperature, precipitation, sunshine 1800-2014

    • cloud.csiss.gmu.edu
    • ckan.mobidatalab.eu
    • +3more
    Updated Jul 10, 2019
    + more versions
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    Netherlands (2019). Climate data De Bilt; temperature, precipitation, sunshine 1800-2014 [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/56886-climate-data-de-bilt-temperature-precipitation-sunshine-1800-2014
    Explore at:
    http://publications.europa.eu/resource/authority/file-type/atom, http://publications.europa.eu/resource/authority/file-type/jsonAvailable download formats
    Dataset updated
    Jul 10, 2019
    Dataset provided by
    Netherlands
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    De Bilt
    Description

    This table presents climate data from the Dutch weather station De Bilt (source: KNMI). The average winter and summer temperatures, which started in 1800, are the longest current series shown in the table. The series on the average year temperature and on hours of sunshine per year started in 1900. For the number of days below of above a certain temperature (ice days, summery days) the ranges started between 1940 and 1950. The complete set of climate data is available from 1980 onwards.

    Data available from: 1800-2014.

    Status of the figures: All data are definite.

    Changes as of 19 April 2016: Not. This table has been discontinued.

    When will new figures be published? Not applicable anymore.

    Data on the weather and climate in The Netherlands can be found on the website of the Royal Netherlands Meteorological Institute KNMI

  19. m

    Meteorological indicator dataset for selected European NUTS 3 regions

    • data.mendeley.com
    Updated May 7, 2020
    + more versions
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    Denitsa Angelova (2020). Meteorological indicator dataset for selected European NUTS 3 regions [Dataset]. http://doi.org/10.17632/sf9x4h5jfk.3
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    Dataset updated
    May 7, 2020
    Authors
    Denitsa Angelova
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and economic outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant economic outcomes are only available on an aggregated level. This dataset intends to aid empirical investigations by providing a dataset with monthly meteorological indicators on a European NUTS 3 regional level for 13 countries for the period from 1989 to 2018.

    We created this dataset from daily data in a grid of 25km x 25km provided by the Joint Research Centre of the European Commission. We matched the map with the raw data to a map with the administrative boundaries of European NUTS3 regions. After appropriately weighting, we calculated the monthly, regional mean, variance and kurtosis of the following variables: daily maximum, minimum, average air temperature in degrees Centigrade, sum of precipitation in mm per day and snow depth in cm. We report the covariance between the average temperature and the precipitation as well.

  20. Temperature and precipitation gridded data for global and regional domains...

    • cds.climate.copernicus.eu
    • cds-stable-bopen.copernicus-climate.eu
    netcdf
    Updated Apr 9, 2025
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    ECMWF (2025). Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations [Dataset]. http://doi.org/10.24381/cds.11dedf0c
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    netcdfAvailable download formats
    Dataset updated
    Apr 9, 2025
    Dataset provided by
    European Centre for Medium-Range Weather Forecastshttp://ecmwf.int/
    Authors
    ECMWF
    License

    https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/insitu-gridded-observations-global-and-regional/insitu-gridded-observations-global-and-regional_15437b363f02bf5e6f41fc2995e3d19a590eb4daff5a7ce67d1ef6c269d81d68.pdfhttps://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/insitu-gridded-observations-global-and-regional/insitu-gridded-observations-global-and-regional_15437b363f02bf5e6f41fc2995e3d19a590eb4daff5a7ce67d1ef6c269d81d68.pdf

    Time period covered
    Jan 1, 1750 - Jan 1, 2021
    Description

    This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

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Statista (2023). European average temperature relative to pre-industrial period 1850-2019 [Dataset]. https://www.statista.com/statistics/888098/europes-annual-temperature-anomaly/
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European average temperature relative to pre-industrial period 1850-2019

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Dataset updated
Apr 19, 2023
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Europe
Description

Europe's average temperature has increased significantly when compared with the pre-industrial period, with the average temperature in 2014 2.22 degrees Celsius higher than average pre-industrial temperatures, the most of any year between 1850 and 2019.

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