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.

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.

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 *** 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.

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.

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.

In March 2024, the surface air temperature anomaly in Europe reached ****°C, relative to the ********* average for that month. This was the second-warmest March on record, just ****°C cooler than March 2014, which was Europe's hottest March in this period.

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 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.

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.

Average over the period 1991-2015 of the annual average of the average daily temperature (°C)

Average rainfall (mm) and average temperature (centigrade) for the North East England and East England Met Office Climate district, which includes Lincolnshire.

This dataset shows the average rainfall in millimetres and average temperature in centigrade by month, year, and meteorological season. It also has an annual figure for each year.

The data is sourced from the UK Met Office website. See the Source link for more information about the data and the area it covers.

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 **.

Gridded files of average monthly minimum temperature in the Netherlands over the period 1981-2010 (normal period). Based on 28 automatic weather stations.

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.

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

This dataset comprises synthetic weather data generated for historical (“control” present, 1985-2014) and two future periods (near future: 2031-2060 (period1) and far future: 2071-2100 (period2)) across a domain encompassing Germany and its neighboring riparian countries. The dataset was produced through the following key steps: (1) Classifying Weather Circulation Patterns for the Observed/Present Period: Weather circulation patterns (CPs) were classified for a European domain (35°N – 70°N, 15°W – 30°E), and regional average temperatures at 2 m height (t2m) were calculated for the German domain (45.125°N – 55.125°N, 5.125°E – 19.125°E). This classification used mean sea level pressure (psl) and mean temperature (tas) data from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al., 2020). (2) Training Non-Stationary Climate-Informed Weather Generator (nsRWG): The nsRWG (Nguyen et al., 2024), conditioned on the classified CPs and using tas as a covariate, was set up and trained for the German domain using the E-OBS dataset, version 25.0e (Cornes et al., 2018). This training dataset includes 540 grid cells of mean daily temperature and precipitation totals for the period 1950–2021, with a spatial resolution of 0.5° x 0.5°. (3) Generating Data for the Present Period: Long-term synthetic data for the present period is generated using the trained nsRWG. (4) Assigning Circulation Patterns for Future Periods: The classified CPs from the present period were assumed to remain stable in the future. These CPs were assigned to future periods based on mean sea level pressure data from nine selected general circulation models (GCMs) from CMIP6 (Eyring et al., 2020) for the two future periods and two shared socio-economic pathways: SSP245 and SSP585 (IPCC, 2023). In total, CPs were derived for 36 scenarios, and regional average temperatures were also computed. (5) Downscaling Data for Future Scenarios: The nsRWG was used to statistically downscale long-term synthetic weather data for all 36 future scenarios. (6) Final dataset: The dataset includes synthetic weather data generated for the present period (Step 3) and future scenarios (Step 5). This dataset is expected to offer a key benefit for hydrological impact studies by providing long-term (thousands of years) consistent synthetic weather data, which is indispensable for the robust estimation of probability changes of hydrologic extremes such as floods.

This dataset provides monthly maximum Land Surface Temperature (LST) values over Europe, derived from 1-km AVHRR observations. The data is generated by DLR and provided in the framework of the TIMELINE project. LST values are retrieved using physically-based split- and mono-window algorithms and corrected for atmospheric influences and surface emissivity. Only cloud-free observations with sensor view angles below 50 degrees are used. Due to reliance on infrared observations, data may be limited under persistent cloud cover. To ensure temporal consistency across sensors and overpass times, an orbit drift correction method was applied. This method harmonizes LST values to a fixed reference time of 13:00 local solar time, approximating the daily maximum temperature. The dataset is gridded in a 1-km LAEA ETRS89 projection. The product is provided in four tiles, covering the extent of the European Environmental Agency (EEA) reference grid, which includes the area from 900 000 m East and 900 000m North to 7 400 000m East and 5 500 000m North.

The TIMELINE (TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long-Term Dynamics In our Natural Environment) project, led by the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), focuses on generating a consistent, multi-decadal time series derived from NOAA and Metop AVHRR data. Spanning more than 40 years from the early 1980s to the present this dataset covers Europe and North Africa. TIMELINE establishes an operational environment for the systematic reprocessing of AVHRR raw data into Level 1b, Level 2, and Level 3 geoinformation products at 1.1 km spatial resolution. These products maintain uniform standards in format, projection, and spatial coverage. The dataset includes a comprehensive suite of land and atmospheric parameters such as atmospherically corrected surface reflectance, NDVI, snow cover, fire hotspots, burnt area, land and sea surface temperatures, and various cloud physical properties (e.g., cloud top temperature). By combining traditional and innovative remote sensing products with robust processing algorithms and state-of-the-art validation techniques, TIMELINE provides a unique, high-quality dataset for global change research.

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.