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.

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.

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.

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.

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 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.

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.

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.

Type	Variable	Percentile	Window	Min duration	Rescaling	Months	Bootstrapping
Heat wave	tmin and tmax	90	5	3	max	6, 7, 8	yes
Cold wave	tmin and tmax	10	5	3	max	1, 2, 10, 11, 12	yes
Flash drought	swvl 0-28cm	30	5	5	mean	6, 7, 8, 9, 10	no
Fire risk	FWI	90	5	1	mean	3, 4, 5, 6, 7, 8, 9	yes

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.

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.

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 **.

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.

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

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.

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

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.

Norway and Finland were the European countries with the highest number of heating degree days in 2022. On the other side of the spectrum, Cyprus and Malta were the countries with the highest number of cooling degree days. That means that the number of degrees that the average temperature of Malta was below ** degrees Celsius added up throughout the whole year amounted to ***. ** degrees Celsius is set as the base temperature under which it would be necessary to heat indoor spaces, while degrees Celsius is set as the base temperature above which it would be necessary to start heating indoor spaces. The countries with the highest numbers have the highest need for space heating or cooling throughout the year.

ECMWF is the European Centre for Medium-Range Weather Forecasts.

Both a research institute and a 24/7 operational service, producing global numerical weather predictions and other data for our Member and Co-operating States and the broader community. The Centre has one of the largest supercomputer facilities and meteorological data archives in the world. Other strategic activities include delivering advanced training and assisting the WMO in implementing its programmes.

A key player in Copernicus, the Earth Observation component of the European Union’s Space programme, offering quality-assured information on climate change (Copernicus Climate Change Service), atmospheric composition (Copernicus Atmosphere Monitoring Service), flooding and fire danger (Copernicus Emergency Management Service), and through the EU's Destination Earth initiative, we are developing prototype digital twins of the Earth.

The organisation was established in 1975 and now employs around 450 staff from more than 35 countries. ECMWF is one of the six members of the Co-ordinated Organisations, which also include the North Atlantic Treaty Organisation (NATO), the Council of Europe (CoE), the European Space Agency (ESA), the Organisation for Economic Co-operation and Development (OECD), and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).

This page contains information how to access data of the ECMWF.

This dataset contains monthly elements measured at our synoptic station in Roches Point, Co Cork.The file is updated monthly. Values for each month include: Precipitation Amount, Mean Air Temperature, Maximum Air Temperature (C), Minimum Air Temperature, Mean Maximum Temperature, Mean Minimum Temperature, Grass Minimum Temperature, Mean Wind Speed, Highest Gust, Sunshine duration.

Seasonal weather forecast to prevail in Cyprus, in the next three months, in which analyses and comparisons of the values of climate average temperature and rain with those expected over the forecast period are presented. The seasonal prognosis has been produced and published since 2010, especially for the region of South-Eastern Europe, by the Hydrometeorological Service of Serbia and is under the auspices of the World Meteorological Organisation. The whole effort is called Southeast Europe Conference for Seasonal Forecast (SEECOF). The model from which this seasonal forecast is produced is dynamic and has many similarities with short weather forecast models. This model also entails uncertainty (like any weather forecast product) that is why it may vary from the actual weather, which on average will prevail every month.

In September 2024, the surface air temperature in Europe was ****°C above the ********* average for that month. This made it Europe's second-warmest September on record, after the previous year's September, which was ****°C above average.