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.

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.

Projected changes in annual mean surface temperature (in K) under A1B scenario, multi-model ensemble mean for the time period 2021-2050 relative to 1961-1990 mean. Map presents changes using ensemble mean of several regional climate models (RCMs), run by different climate modelling communities in the frame of the EU FP6 Integrated Project ENSEMBLES (Contract number 505539). Data are presented as changes in relative terms (according to 1961-1990 period) in spatial resolution of approximately 25 km.

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 with a geographic location of Northern Hemisphere. The time period coverage is from 185 to -50 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

1. Ambient temperatures in natural environments can vary widely over short distances, especially on rugged ground where exposure to solar radiation depends on slope and aspect. Temperatures can also fluctuate rapidly, reaching values not revealed by climate data. Such fine-scale variation raises challenges for modelling species distributions under changing climates. 2. To avoid misunderstanding current species distributions and future changes, temperatures must be modelled at high resolutions. Most existing methods either require extensive parameterisation or are pre-parameterised for restricted localities and current conditions; here we describe a more versatile method intended for European landscapes under a wide range of scenarios. 3. The availability of high-resolution topographic data makes possible the use of projected solar irradiation to help predict local diurnal ground temperatures. Using time series from 83 points across Europe, we fitted statistical models for soil surface temperature based on geographical characteristics of the sites along with atmospheric variables obtained from a publicly-available database. The sites ranged from 40°N to 60°N and 2°W to 25°E, and from 43m to 1500m in elevation. 4. We compare models for monthly mean and daily afternoon temperatures, for open and tree-covered habitats. The effect of topography was greatest for the daily predictions, and generally more important in open than in tree-covered sites. Tests with data collected from other European locations in a different time period suggest that our models can predict monthly means with an error standard deviation of 2.7°C. We provide an R function that implements our models on the basis of readily-available data. 5. Our mean-temperatures model should be useful for understanding organisms' niches, dispersal possibilities and community dynamics, and for predicting species' refugia, range shifts and opportunities for adaptation under projected climate change.

The raster dataset of urban heat island modelling shows the fine-scale (100m pixel size) temperature differences (in degrees Celsius °C) across 100 European cities, depending on the land use, soil sealing, anthropogenic heat flux, vegetation index and climatic variables such as wind speed and incoming solar radiation.

In the framework of the Copernicus European Health contract for the Copernicus Climate Change Service (C3S), VITO provided 100m resolution hourly temperature data (2008-2017) for 100 European cities, based on simulations with the urban climate model UrbClim (De Ridder et al., 2015). As the cities vary in size, so do the model domains. They have been defined with the intention to have a more or less constant ratio of urban vs. non-urban pixels (as defined in the CORINE land use map), with a maximum of 400 by 400 pixels (due to computational restraints). From this data set, the average urban heat island intensity is mapped for the summer season (JJA), which is the standard way of working in the scientific literature (e.g. Dosio, 2016). The UHI is calculated by subtracting the rural (non-water) spatial P10 temperature value from the average temperature map.

The 100 European cities for the urban simulations were selected based on user requirements within the health community.

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.

A set of 12 monthly maps of mean air temperature (see documentation for details).

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.

Sheet 1 - Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) data with uncertainty and latitude-longitude

Sheet 2 - References for this data

The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the “bar coded” images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/

This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data and Frieler et al. (2024).

The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:

bioclim01 = Annual Mean Temperaturebioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))bioclim03 = Isothermality (BIO2/BIO7) (×100)bioclim04 = Temperature Seasonality (standard deviation ×100)bioclim05 = Max Temperature of Warmest Monthbioclim06 = Min Temperature of Coldest Monthbioclim07 = Temperature Annual Range (BIO5-BIO6)bioclim08 = Mean Temperature of Wettest Quarterbioclim09 = Mean Temperature of Driest Quarterbioclim10 = Mean Temperature of Warmest Quarterbioclim11 = Mean Temperature of Coldest Quarterbioclim12 = Annual Precipitationbioclim13 = Precipitation of Wettest Monthbioclim14 = Precipitation of Driest Monthbioclim15 = Precipitation Seasonality (Coefficient of Variation)bioclim16 = Precipitation of Wettest Quarterbioclim17 = Precipitation of Driest Quarterbioclim18 = Precipitation of Warmest Quarterbioclim19 = Precipitation of Coldest Quarterhuss = Average (arithmetric mean) specific humidityrsds = Average (arithmetric mean) Surface downwelling shortwave radiationsfcwind = Average near-surface wind speed (arithmetric mean)---Data properties:

Shared Socioeconomic Pathways (SSP) SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5

General circulation models (GCMs) GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL

Spatial grain 0.5 degree (~50km²)

Geographic projection WGS 84

Temporal grain 10 year steps

Spatial extent Continental Europe including Turkey (see screenshot)

Temporal extent 1850 to 2010 (Historical), 2010 - 2100 (Future)

Number of variables 22

All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse.

The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods.

This database contains information on natural outflowing thermal springs across Europe. Outflowing temperature is at least 5ºC higher that the annual mean air temperature of the site.

This catalogue entry provides gridded data from global (CMIP5 and CMIP6) and regional (CORDEX) projections for the set of 22 variables and indices included in the IPCC Interactive Atlas, a novel contribution from Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6). These variables and indices are relevant for the climatic impact-drivers used in the regional assessments conducted in AR6 (Chapters 10, 11, 12 and Atlas), related to heat and cold, wet and dry, snow and ice, and wind. This dataset is particularly intended for Climate Data Store (CDS) users who want to develop customised products not directly available from the IPCC Interactive Atlas (e.g. regional information at national or subnational scales). This dataset includes gridded information with monthly/annual temporal resolution for historical experiments and climate projections based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios for CMIP5/6 and CORDEX multi-model ensembles for the 22 variables and indices (computed from daily data). The ensembles are harmonised using regular grids with horizontal resolutions of 2° (CMIP5), 1° (CMIP6), 0.5° (CORDEX), and 0.25° (European CORDEX domain); details on the particular ensembles for each dataset are included in the documentation links. This dataset allows the reproduction, expansion and customisation of the climate change products displayed in the IPCC Interactive Atlas. This includes the global/continental maps of CMIP/CORDEX climate changes (for future periods across scenarios or for global warming levels, e.g. +2°C), and the regionally-aggregated time series, scatter plots, or global warming level plots. Related datasets, also available through the CDS, include the CMIP5/6 global climate projections and the CORDEX regional climate projections. The original CMIP and CORDEX data was produced by the institutions and modelling centres participating in these initiatives, as described in AR6 WGI Annex II, with partial support from different programmes, including support from Copernicus for some of the EURO-CORDEX runs and for data curation and publication of world-wide CORDEX datasets. As a result, the dataset is fully reproducible from the CDS for CORDEX, but not for CMIP (some models and versions are different in the CDS and the Atlas ensembles).
This dataset is distributed as part of the IPCC-DDC Atlas products under a Creative Commons Attribution 4.0 International License (CC-BY 4.0) and Copernicus has supported the standardisation and technical curation.

Metadata: Title: Rainfall erosivity in Europe Description: This map provides a complete rainfall erosivity dataset for European Union (28 member States) and Switzerland based on REDES database with high temporal resolution rainfall measurements of 26,394 years. Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map. REDES is provided as a point database including R-factor for each of the 1,675 stations (see below). Monthly R-factor maps are also available (see below) R-factor detailed assessments for Greece and Switzerland are available (see below). Future projections (2050) of R-factor are available (see below). Spatial coverage: European Union (28 Countries) & Switzerland Pixel size: 500m Measurement Unit: MJ mm ha-1 h-1 yr-1 Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage: 40 years - Predominant in the last decade: 2000 - 2010 Users can downloads Raw data, Baseline map (2010), Monthly erosivity, Future projections (2050), Past erosivity (1961-2000). Note: We also make available the Global Rainfall Erosivity (GloREDa) database R-factor in Europe The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,675 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. Info: Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Begueria, S., Alewell, C. 2015. Rainfall erosivity in Europe. Sci Total Environ. 511: 801-814. REDES: Rainfall Erosivity Database on the European Scale The Rainfall Erosivity Database on the European Scale (REDES) includes high temporal resolution precipitation data and the claculated R-factor from 1,675 precipitation stations within the European Union (EU) and Switzerland. The Rainfall Erosivity Database on European Scale (REDES) of precipitation stations is the result of calculating the R-factor for a total of 26,394 years with a mean value of 17.1 years per station. The data collection exercise of high temporal resolution data began in March 2013 and was concluded in May 2014. Data for additional 134 stations were collected in 2015. For the present rainfall erosivity data collection exercise, a participatory approach has been followed in order to collect data from all Member States (Aknowledgments). The precipitation data collected from the 28 countries across Europe have different temporal resolutions: 60-min, 30-min, 15-min, 10-min and 5-min. In order to homogenise the R-factor results calculated using different time-step data, conversion factors were established to have the data at the 30-min temporal resolution (reference scale). Info: Panagos et al., 2015; Borrelli et al., 2016; Panagos et al., 2016; Ballabio et al., 2017; Meuburger et al., 2012 Future Erosivity projections in 2050 based on climate change The rainfall erosivity in 2050 was modelled based on on a moderate climate change scenario (HadGEM RCP 4.5) and using as main data sources the REDES based European R-factors and as covariates the WorldClim climatic datasets. Although the rainfall erosivity projections are based on many uncertainties, this pan-European spatial estimation highlights the areas where rainfall erosivity is projected to undergo substantial changes. The predicted mean increase in R-factor is expected also to increase the threat of soil erosion in Europe. However, climate change might substantially affect land cover and land use, which might counterbalance or enhance some erosional trends. The most prominent increases of R-factors are predicted for North-Central Europe, the English Channel, The Netherlands and Northern France. On the contrary, parts of the Mediterranean basin show a decrease of rainfall erosivity. he mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha-1 h-1yr-1 till 2050 showing a relative increase of 18% compared to baseline data (2010).The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models. Info: Panagos, P., Ballabio, C., Meusburger, K., Spinoni, J., Alewell, C., Borrelli, P. 2017. Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets. Journal of Hydrology, 548: 251-262. Monthly Rainfall Erosivity in Europe The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha-1 h-1) compared to winter (87 MJ mm ha-1 h-1). The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sumof all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areaswith highest risk of soil losswhere conservationmeasures should be applied in different seasons of the year. Info: Ballabio, C., Borrelli, P. , Spinoni, J., Meusburger, K., Michaelides, S., Begueria, S., Klik, A., Petan, S., Janecek, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Tadić, M.P., Nazzareno, D., Kostalova, J., Rousseva, S., Banasik, K., L., Alewell, C. , Panagos, P. 2017. Mapping monthly rainfall erosivity in Europe. Sci Total Environ. 579: 1298-1315 Past Erosivity and trend detection (1961-2018) In this study we reconstructed past rainfall erosivity in Europe for the period 1961–2018, with the aim to investigate temporal changes in rainfall erosivity. As input data, we used the Rainfall Erosivity Database at European Scale (REDES) and Uncertainties in Ensembles of Regional Reanalyses (UERRA) rainfall data. Using a set of regression models, which we derived with the application of the k-fold cross-validation approach, we computed the annual rainfall erosivity for the 1675 stations forming the REDES database. Based on the reconstructed data, we derived a rainfall erosivity trend map for Europe where the results were qualitatively validated. Among the stations showing a statistically significant trend, we observed a tendency towards more positive (15%) than negative trends (7%). In addition, we also observed an increasing tendency of the frequency of years with maximum erosivity values. Geographically, large parts of regions such as Eastern Europe, Scandinavia, Baltic countries, Great Britain and Ireland, part of the Balkan Peninsula, most of Italy, Benelux countries, northern part of Germany, part of France, among others, are characterized by a positive trend in rainfall erosivity. By contrast, negative trends in annual rainfall erosivity could be observed for most of the Iberian Peninsula, part of France, most of the Alpine area, Southern Germany, and part of the Balkan Peninsula, among others. The new dataset of rainfall erosivity trends reported in this study scientifically provides new information to better understand the impacts of the ongoing erosivity trends on soil erosion across Europe, while, from a policy perspective, the gained findings provide new knowledge to support the development of soil erosion indicators aiming at promoting mitigation measures at regional and pan-European level. Info: Bezak, N., Ballabio, C., Mikoš, M., Petan, S., Borrelli, P., Panagos, P. 2020. Reconstruction of past rainfall

This map shows the annual average cultural impact (impact on architectural heritage) of floods due to intense precipitation in future climate (with climate projection 2050). The cultural risk is calculated as a weighted combination of the 3 cultural damage maps with high, medium and low probability, expressed in a relative score/year per architectural heritage.

Monthly surface temperature scale heights, tsch, are provided as digital maps from −90° N to +90° N in latitude and from −180° E to +180° E in longitude at a resolution of 0.25° in latitude and longitude. The value of the monthly surface temperature scale height, tsch, for the selected month at any desired location on the surface of the Earth can be calculated using the relevant interpolation method in section 2 of Recommendation ITU-R P.2145-0. The integral maps referenced by this Recommendation were derived from various data products from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Climate Change Service. Neither the European Commission nor ECMWF is responsible for the use or application of these maps.