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.

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.

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 dataset contains monthly averages of the QQ variable, representing cloud cover or a related atmospheric parameter, across Europe. It spans multiple years and regions, enabling analysis of spatial and temporal variability in cloud conditions. The data support climate monitoring, trend analysis, and research into links between cloud cover and broader environmental or climatic patterns. The dataset’s standardized format facilitates integration with other European climate datasets, including temperature, precipitation, and wind speed measurements.

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 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.

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

DATA DESCRIPTION: Temperature averaged across Europe, UDATP, 1914-1920 C.E. Citation: Alexander F. More, Christopher P. Loveluck, Heather Clifford, Michael J. Handley, Elena V. Korotkikh, Andrei V. Kurbatov, Michael McCormick and Paul A. Mayewski. (2020). The Impact of a six-year climate anomaly on the 'Spanish Flu' Pandemic and WWI. GeoHealth, American Geophysical Union. Coverage: Values averaged across Europe, Latitude 37N-56N; Longitude 0E-30E) DATE/TIME START: January 1914 * DATE/TIME END: December 1920 "Source: University of Delaware Air Temperature and Precipitation (UDATP). File generated by Climate Reanalyzer, Climate Change Institute; University of Maine; USA (http://climatechange.umaine.edu) " Comment: PLEASE CITE ORIGINAL SOURCE WHEN USING THIS DATA. Dataset corresponds to Figure 2 (Marine air influx and total deaths in Europe 1914-1920) in the final manuscript. Precipitation (mm) for Western Europe 1914-1920 Parameter(s): Temperature averaged across Europe at 2 meters (degrees Celsius) Year C.E.

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.

By the year 2050, the temperature of the warmest month in Ljublijana is expected to increase by eight degrees Celsius. This is the biggest increase when compared to the other European Union capital cities. The capital cities of both France and Germany are expected to see temperatures rise by *** degrees Celsius. With temperatures expected to increase, the possibility of wildfires increases. As of May 2019, countries such as France and Spain had suffered far more wildfires than the 2008 to 2018 average.

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

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.

File descriptions

weather_prediction_dataset.csv - Main data file, tabular data, comma-separated CSV. Contains the data for different weather features (daily observations, see below for more details) for 18 European cities or places through the years 2000 to 2010. **weather_prediction_picnic_labels.csv **- Optional data to be used as potential labels for classification tasks. Contains booleans to characterize the daily weather conditions as suitable for a picnic (True) or not (False) for all 18 locations in the dataset. **metadata.txt **- Further information on the dataset, the data processing, and conversion, as well as the description and units of all weather features.

Huber, F., van Kuppevelt, D., Steinbach, P., Sauze, C., Liu, Y., & Weel, B. (2022). Weather prediction dataset (Version v5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7525955

(1) Output of the Renewable Energy Model (REM) as described in Insights into weather-driven extremes in Europe’s resources for renewable energy (Ho and Fiedler, 2024), last modification on 30.10.2023 from Linh Ho, named year_PV_wind_generation_v2.nc, with 23 years from 1995 to 2017. REM includes one simulation of photovoltaic (PV) power production and one simulation of wind power production across European domain, with a horizontal resolution of 48 km, hourly output for the period 1995--2017.

The output has a European domain with the same size as in the reanalysis dataset COSMO-REA6. This is a rotated grid with the coordinates of the rotated North Pole −162.0, 39.25, and of the lower left corner −23.375, −28.375. See Bollmeyer et al. (2014, http://doi.org/10.1002/qj.2486). Data downloaded from https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/

(2) Weather pattern classification daily for Europe from 1995 to April 2020, named EGWL_LegacyGWL.txt, from James (2007, http://doi.org/10.1007/s00704-006-0239-3)

(3) The installation data of PV and wind power in Europe for one scenario in 2050 from the CLIMIX model, processed to have the same horizontal resolution as in REM, named installed_capacity_PV_wind_power_from_CLIMIX_final.nc. Original data were provided at 0.11 degree resolution, acquired from personal communication with the author from Jerez et al. (2015, http://doi.org/10.1016/j.rser.2014.09.041)

(4) Python scripts of REM, including: - model_PV_wind_complete_v2.py: the main script to produce REM output - model_PV_wind_potential_v2.py: produce potential (capacity factor) of PV and wind power for model evaluations, e.g., against CDS and Renewables Ninja data, as descript in Ho and Fiedler (2024) - model_PV_wind_complete_v1_ONLYyear2000.py: a separate Python script to produce REM output only for the year 2000. Note that the data for 2000 from COSMO-REA6 were read in a different approach (using cfgrib) probably due to the time stamp changes at the beginning of the milenium, also explains the larger size of the final output - utils_LH_archive_Oct2022.py: contains necessary Python functions to run the other scripts

(5) Jupyter notebook files to reproduce the figures in Ho and Fiedler (2024), named Paper1_Fig*_**.ipynb

(6) Time series of European-aggregated PV and wind power production hourly during the period 1995--2017, processed data from the dataset (1) to facilitate the reproduction of the figures, including two installations scale-2019 and scenario-2050: - Timeseries_all_hourly_1995_2017_GW_scale2019.csv - Timeseries_all_hourly_1995_2017_GW_scen2050.csv

This dataset provides precipitation and near surface air temperature for Europe as Essential Climate Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. CIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector. The ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy. The data represent the current state-of-the-art in Europe for regional climate modelling and indicator production. Data from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11° x 0.11° and 5km x 5km. The ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations as a reference dataset. Note these are model output data, not observation data as is the general case for ECVs. The CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 "degree scenario" periods defined by when global warming exceeds a given threshold (1.5 °C, 2.0 °C or 3.0 °C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM. This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.

This dataset contains key characteristics about the data described in the Data Descriptor ClimateEU, Scale-free climate normals, historical time series, and future projections for Europe. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

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 with a geographic location of Europe. The time period coverage is from 1350 to -57 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The TCSP European Centre for Medium-Range Weather Forecasts (ECMWF) dataset consists of three-hour forecast/analysis data for the Tropical Cloud Systems and Processes (TCSP) field campaign, supplied by ECMWF. The TCSP field campaign was conducted from July 1 through July 27, 2005 out of the Juan Santamaria Airfield in San Jose, Costa Rica. TCSP collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind, and air pressure that creates ideal birthing conditions for tropical storms, hurricanes, and related phenomena. The goal of this mission was to help better understand how hurricanes and other tropical storms are formed and intensify. The ECMWF three-hour forecast/analysis data are in a gridded binary (GRIB) format and tarred into daily files.