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.

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.

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.

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.

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.

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 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 17.

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.

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

Monthly and annual average temperatures ((7+ 19+max+min)/4) from 1971

This data is the result v001 of the DecReg /MiKlip regionalization project. The goal was a long-term stable data set to validate decadal climate forecasts. Due to an error in the NetCDF header, V001 is replaced by the corrected version V002. ature_mean_miklip_decreg_de.pdf

Overview: 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.

Surface temperature: Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes.

Processing steps: The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution 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 difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA

The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis (starting from Saturday) for the time period 2016 - 2020. Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of surface temperature.

File naming: Average of daily average: era5_land_ts_avg_weekly_YYYY_MM_DD.tif Max of daily max: era5_land_ts_max_weekly_YYYY_MM_DD.tif Min of daily min: era5_land_ts_min_weekly_YYYY_MM_DD.tif

The date in the file name determines the start day of the week (Saturday).

Pixel values: °C * 10 Example: Value 302 = 30.2 °C

The QML or SLD style files can be used for visualization of the temperature layers.

Coordinate reference system: ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)

Spatial extent: north: 82N south: 18S west: -32W east: 61E

Spatial resolution: 1 km

Temporal resolution: weekly

Time period: 01/01/2016 - 12/31/2020

Format: GeoTIFF

Representation type: Grid

Software used: GRASS 8.0

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

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

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

Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.

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.

This dataset contains the data displayed in the figures or the article "High-resolution projections of ambient heat for major European cities using different heat metrics".

The different files contain:

Data_Fig1_DeltaTXx_EURO-CORDEX_1981-2010_to_3K-European-warming_RCP85.nc: Change of yearly maximum temperature in Europe between 1981-2010 and 3 °C European warming relative to 1981-2010.

Data_Fig2_timeseries-GSAT-ESAT_EURO-CORDEX_CMIP5_CMIP6_1971-2100_RCP85_SSP585.xlsx: Time series of global mean surface air temperature (GSAT) for CMIP5 and CMIP6 models, and for European mean surface air temperature (ESAT) for EURO-CORDEX, CMIP5, and CMIP6 models for the period 1971-2100.

Data_Fig3_TX-distribution_distance-from-city-centre_E-OBS_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for E-OBS for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_ERA5-Land_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for ERA5-Land for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_EURO-CORDEX_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for the EURO-CORDEX models for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig3_TX-distribution_distance-from-city-centre_weather-stations_1981-2010.xlsx: Distribution of average daily maximum temperature in summer (June, July, August) in 1981-2010 for GSOD and ECA&D stations for all investigated cities. Temperature data are indicated as a function of the distance to the city centre.

Data_Fig4_TX-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.

Data_Fig5_Contribution-of-explanatory-variables-to-total-explained-variance.xlsx: Contribution of different explanatory variables (climate and location factors) to the total explained variance of spatial patterns of heat metrics.

Data_Fig6_TN-ambient-heat_EURO-CORDEX_3K-European-warming.xlsx: Nighttime heat metrics for the investigated cities: HWMId-TN at 3 °C European warming relative to 1981-2010, TN exceedances above 20 °C at 3 °C European warming relative to 1981-2010, and TNx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for EURO-CORDEX models.

Data_Fig7_TX-ambient-heat_CMIP5_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP5 models.

Data_Fig7_TX-ambient-heat_CMIP6_3K-European-warming.xlsx: Daytime heat metrics for the investigated cities: HWMId-TX at 3 °C European warming relative to 1981-2010, TX exceedances above 30 °C at 3 °C European warming relative to 1981-2010, and TXx change between 1981-2010 and 3 °C European warming relative to 1981-2010 for CMIP6 models.

Data_Fig8_GCM-RCM-matrix_ambient-heat_3K-European-warming.xlsx: GCM-RCM matrices for the three heat metrics.

Difference between the maximum and minimum annual average temperature

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.

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.

This data is the result of the DecReg /MiKlip regionalization project and serves as a long-term stable dataset for the validation of decadal climate forecasts /DESCRIPTION_gridseu_monthly_air_temperature_mean_MIKLIP_DECREG_v002_en.pdf