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.

In 2024, the average summer temperature in Germany was 18.5 degrees Celsius. This was basically unchanged compared to the year before. While figures fluctuated during the given timeline, there were regular peaks, and in general, temperatures had grown noticeably since the 1960s. Not beating the heat German summers are getting hotter, and as desired as warm weather may be after months of winter (which, incidentally, also warms up year after year), this is another confirmation of global warming. Higher summer temperatures have various negative effects on both nature and humans. Recent years in Germany have seen a growing number of hot days with a temperature of at least 30 degrees, with 11.5 recorded in 2023. However, this was a decrease compared to the year before. The number of deaths due to heat and sunlight had peaked in 2015. Rain or shine All the German states saw less sunshine hours in 2023 compared to the previous year. The sunniest states were Baden-Württemberg, Bavaria and Saarland. Meanwhile, summer precipitation in Germany varied greatly during the same timeline as presented in this graph, but 2022 was one of the dryest years yet.

This statistic shows the average temperature in Germany in the summers of 2023 and 2024, by federal state. In summer 2024, the average temperature in Berlin was 19.7 degrees Celsius. This made Berlin the warmest federal state in the period in question, followed by Brandenburg and Baden-Württemberg.

In 2023/2024, the average winter temperature in Germany was 4.1 degrees Celsius. That winter was part of a growing list of warmer winters in the country. Figures had increased noticeably compared to the 1960s.

Warmer in the winter

Everyone has a different perception of what actually makes a cold or warm winter, but the fact is that winter temperatures are, indeed, changing in Germany, and its 16 federal states are feeling it. Also in 2022/2023, Bremen and Hamburg in the north recorded the highest average figures at around 4 degrees each. The least warm states that year, so to speak, were Thuringia, Saxony, and Bavaria. The German National Meteorological Service (Deutscher Wetterdienst or DWD), a federal office, monitors the weather in Germany.

Global warming

Rising temperatures are a global concern, with climate change making itself known. While these developments may be influenced by natural events, human industrial activity has been another significant contributor for centuries now. Greenhouse gas emissions play a leading part in global warming. This leads to warmer seasons year-round and summer heat waves, as greenhouse gas emissions cause solar heat to remain in the Earth’s atmosphere. In fact, as of 2022, Germany recorded 17.3 days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

This graph shows the average monthly precipitation in Germany from February 2024 to February 2025. In February 2025, the average precipitation amounted to 24 liters per square meter, an increase compared to the previous month. The rainiest state in Germany was Saarland.

Overview

These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.

The base rasters from DWD are:

HYRAS precipitation

REGNIE precipitation

DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)

To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.

Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.

The following image illustrates the generation of the refined rasters on a small example window:

This statistic displays the average maximum monthly temperature in Germany over the past 20 years. It shows that over the past twenty years the month with the highest average maximum temperature has been July, with an average temperature of 22.4 degrees Celsius. On average, January has been the coldest month.

The annual average spring temperature in Germany was 8.9 degrees Celsius during the 1991-2020. Figures increased during each decade displayed, in each season.

Quality controlled and gap-filled air temperature and atmospheric humidity dataset from the street-level weather sensor network (WSN) in Freiburg i. Br., Germany for the period 2022-09-01 to 2023-08-31 as described in:

Plein M, Feigel G, Zeeman M, Dormann C, Christen A (2025, in review): Using Extreme Gradient Boosting for gap-filling to enable year-round analysis of spatial temperature and humidity patterns in an urban weather station network in Freiburg, Germany. in review.

Hourly gap-filled values

The file "Freiburg_AWS_20220901_20230831_gap_filled_data_ta_rh_Plein_et_al.csv" contains gap-filled hourly air temperature and relative humidity time series from 41 stations of the street-level weather sensor network (WSN) in Freiburg i. Br., Germany from 1 Sep 2022 to 31 Aug 2023 with the following field descriptors:

• "datetime_UTC" the time stamp of the measured value in the format YYYY-MM-DDTHH:II:SSZ where Y = year, M = month, D = day of month, H = hour, I = minute, S = second in UTC attributing the start of the averaging interval.
• "station_id" - 6 letter code of WSN (FR for Freiburg and last 4 letters for station name, see also https://doi.org/10.5281/zenodo.12732552). The station FRTECH is not included.
• "variable" - the variable ("Ta_degC" for air temperature in ºC or "RH_percent" for relative humidity in %).
• "value" - the numeric value of the measurement.
• "data_type" - either "observed" (i.e. measured) or "imputed" (i.e. gap-filled using the Extreme Gradient Boosting method).

Annual statistics per station

The files "Freiburg_AWS_20220901_20230831_annual_statistics_per_station_Plein_et_al" (in csv and xlsx Format) contain annual summary statistics based on the gap-filled hourly air temperature and relative humidity time series of the street-level weather sensor network (WSN) in Freiburg i. Br., Germany from 1 Sep 2022 to 31 Aug 2023 and from two official DWD stations in Freiburg with the following field descriptors:

• "station_id" - 6 letter code of weather station (FR for Freiburg and last 4 letters for station name, see also https://doi.org/10.5281/zenodo.12732552). The two official DWD stations are also included (No. 01443 on the airfield and No. 13667 in the city centre).
• "station_name" - Full human-readable name of weather station.
• "latitude_degN" - Latitude of site in decimal degrees North.
• "longitude_degE" - Longitude of site in decimal degrees East.
• "elevation_masl" - Elevation of site in metres above mean sea level.
• "mean_ta_degC" - Annual average air temperature in the period 2022-09-01 to 2023-08-31 in ºC.
• "mean_rh_percent" - Annual average relative humidity in the period 2022-09-01 to 2023-08-31 in %.
• "mean_vp_kPa" - Annual average vapour pressure in the period 2022-09-01 to 2023-08-31 in kPa based on Tetens equation.
• "mean_vpd_Pa"- Annual average vapour pressure deficit in the period 2022-09-01 to 2023-08-31 in Pa based on Tetens equation.
• "sum_summer_day_per_year" - Annual number of summer days (maximum air temperature greater or equal to 25ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_hot_day_per_year" - Annual number of hot days (maximum air temperature greater or equal to 30ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_desert_day_per_year" - Annual number of desert days (maximum air temperature greater or equal to 35ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_tropical_night_per_year" - Annual number of tropical nights (minimum nocturnal air temperature greater or equal to 20ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_frost_day_per_year" - Annual number of frost days (minimum air temperature lower than 0ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_ice_day_per_year" - Annual number of ice days (maximum air temperature lower than 0ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_hottest_station_ranking_per_year" - Annual number of days this station was the station with the highest recorded air temperature in the period 2022-09-01 to 2023-08-31.
• "sum_coldest_station_ranking_per_year" - Annual number of days this station was the station with the lowest recorded air temperature in the period 2022-09-01 to 2023-08-31.

Station descriptions

Details on the stations can be found in the sensor network documentation:

• Plein M, Kersten F, Zeeman M, Christen A (2024): Street-level weather station network in Freiburg, Germany: Station documentation (1.0) Zenodo. https://doi.org/10.5281/zenodo.12732552

Code availability

The code used for imputation of missing values is documented and available here:

• Plein M, Feigel G, Zeeman M, Dormann C, Christen A (2024): Code Supporting the Publication "Using Extreme Gradient Boosting for Gap-Filling to Enable Year-Round Analysis of Spatial Temperature and Humidity Patterns in an Urban Weather Station Network in Freiburg, Germany." (1.0.0) Zenodo. https://doi.org/10.5281/zenodo.14536824

description: Average Summer (Jul-Sep) Temperature (2015-2030) simulated by RegCM3 with ECHAM5 projections as boundary conditions. Units are degrees Celsius. These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid. RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http://users.ictp.it/RegCNET/model.html), and the ICTP RegCM publications web site (http://users.ictp.it/~pubregcm/RegCM3/pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels. RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http://regclim.coas.oregonstate.edu/. Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http://wwwpcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php.; abstract: Average Summer (Jul-Sep) Temperature (2015-2030) simulated by RegCM3 with ECHAM5 projections as boundary conditions. Units are degrees Celsius. These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid. RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http://users.ictp.it/RegCNET/model.html), and the ICTP RegCM publications web site (http://users.ictp.it/~pubregcm/RegCM3/pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels. RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http://regclim.coas.oregonstate.edu/. Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http://wwwpcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php.

The Grosszastrow climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The agrometeorological station Grosszastrow was installed in 2011. It is located on a slightly elevated position next to a natural depression. Closest obstacle is a tree on the opposite site of the depression, approx. 25m from the station. The station is equipped with sensor for measuring the following variables: AdconTR1_Temperature, AdconTR1_RelativeHumidity, AdconRainGauge_Precipitation, AdconWindspeed_Windspeed, AdconWinddirection_Winddirection, AdconBP1_BarometricPressure, KuZCMP3_PyranometerIncoming, KuZCMP3_PyranometerOutgoing, KuZCGR3_PyrgeometerIncoming, KuZCGR3_PyrgeometerOutgoing, UMSTH3_Soiltemperature005cm, UMSTH3_Soiltemperature010cm, UMSTH3_Soiltemperature020cm, UMSTH3_Soiltemperature030cm, UMSTH3_Soiltemperature050cm, UMSTH3_Soiltemperature100cm, AdconSM1_Soiltemperature015cm, AdconSM1_Soiltemperature025cm, AdconSM1_Soiltemperature045cm, AdconSM1_Soiltemperature015-025cm, AdconSM1_Soiltemperature045-075cm, AdconSM1_Soiltemperature075cm, AdconSM1_Soilmoisture010cm, AdconSM1_Soilmoisture020cm, AdconSM1_Soilmoisture030cm, AdconSM1_Soilmoisture040cm, AdconSM1_Soilmoisture050cm, AdconSM1_Soilmoisture060cm, AdconSM1_Soilmoisture070cm, AdconSM1_Soilmoisture080cm, AdconSM1_Soilmoisture090cm, AdconSM1_Soilmoisture100cm, AdconWET_LeafWetness and KuZCGR3_PyrgeometerIncoming The current version of this dataset is 2.1. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. A detailed overview on all changes is provided in the station description file. The version 1.0 is available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.291 containing the measured data and Version 2.0 contains additionally the quality flags for each measured value and extended metadata. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. Data processing was done using DMRP version: 0.5.12. Metadataprocessing was done using DMETA version: 0.3.17. The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (19812010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich FZJ, Helmholtz Centre for Environmental Research UFZ, Karlsruhe Institute of Technology KIT, Helmholtz Zentrum München - German Center for Environmental Health HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.

The Ueckeritz climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The agrometeorological station Ueckeritz was installed in 2013. It is located on the eastern border of a natural sink, with some bushes on the western slope of the sink. The station is equipped with sensor for measuring the following variables: AdconTR1_Temperature, AdconTR1_RelativeHumidity, AdconRainGauge_Precipitation, AdconWindspeed_Windspeed, AdconWinddirection_Winddirection, AdconBP1_BarometricPressure, KuZCMP3_PyranometerIncoming, KuZCMP3_PyranometerOutgoing, KuZCGR3_PyrgeometerIncoming, KuZCGR3_PyrgeometerOutgoing, UMSTH3_Soiltemperature005cm, UMSTH3_Soiltemperature010cm, UMSTH3_Soiltemperature020cm, UMSTH3_Soiltemperature030cm, UMSTH3_Soiltemperature050cm, UMSTH3_Soiltemperature100cm, AdconSM1_Soiltemperature015cm, AdconSM1_Soiltemperature045cm, AdconSM1_Soilmoisture010cm, AdconSM1_Soilmoisture020cm, AdconSM1_Soilmoisture030cm, AdconSM1_Soilmoisture040cm, AdconSM1_Soilmoisture050cm, AdconSM1_Soilmoisture060cm, AdconWET_LeafWetness and KuZCGR3_PyrgeometerIncoming The current version of this dataset is 2.1. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. A detailed overview on all changes is provided in the station description file. The version 1.0 is available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.277 containing the measured data and Version 2.0 contains additionally the quality flags for each measured value and extended metadata. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. Data processing was done using DMRP version: 0.5.12. Metadataprocessing was done using DMETA version: 0.3.17. The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (19812010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich FZJ, Helmholtz Centre for Environmental Research UFZ, Karlsruhe Institute of Technology KIT, Helmholtz Zentrum München - German Center for Environmental Health HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.

STANE BOROVNIK hails from Velenje, a small town with a labor-oriented community that gained significance after World War II when large coal deposits were discovered. The subsequent establishment of a coal mine spurred rapid growth, attracting young families and leading to a relatively higher standard of living compared to other areas in Slovenia and Yugoslavia. Despite the town's prosperity, access to public services, schools, and cultural amenities was somewhat limited. During the late 1960s, Stane attended elementary and high school amidst a cultural shift influenced by Western music, particularly rock, rock and roll, and jazz. Stane became interested in town twinning and youth exchange programs after witnessing German visitors in Velenje and hearing about Velenje youths visiting Esslingen. This led to Stane's involvement in coordinating international youth exchanges among twin towns, including Esslingen, Schiedam, Vienne, Neath, and Norrköping. Despite initial reluctance due to political affiliations, Stane agreed to lead the initiative under the condition of autonomy and sufficient resources. Stane's parents had contrasting experiences during World War II. Stane's mother, growing up on a farm near Velenje, found herself caught in a precarious situation, as her home served as a passageway for both partisans and Germans. Despite her initial confusion, she eventually sided with the partisans, working as a courier and engaging in underground activities. However, she was later caught by the Germans and imprisoned in Austria, facing false accusations of collaboration upon her release. Conversely, Stane's father, living on a mountain farm south of Velenje, had fewer encounters with German forces but steadfastly supported the partisans. His family provided assistance whenever possible, offering food and shelter to partisan fighters. Despite their support for the resistance, neither of Stane's parents were members of the communist party, and they harbored resentment towards the party's post-war treatment. Stane's family initially held negative perceptions of Germany, viewing it as a source of evil due to the wartime experiences and propaganda. Therefore, the idea of visiting Germany didn't hold much appeal to them, and they expressed displeasure when Stane returned from trips to Germany. However, as Stane continued to visit Germany and share their experiences, their family gradually softened their stance. They began to listen and learn about the realities of contemporary Germany, gaining a more nuanced understanding beyond the historical context. Over time, Stane's family became more receptive to the idea of visiting Germany and exploring its culture and society. The 1970s marked a period of flourishing for Europe, characterized by a sense of unity and increased mobility. Memories of war began to fade, and the concept of traveling across borders became more feasible, especially with the advent of air travel. Stane emphasizes the importance of viewing Europe as a united family, transcending political divisions.In organizing youth exchanges, Stane encouraged participants to focus on commonalities rather than political differences, urging them to refrain from engaging in political debates. By focusing on everyday life and human connections, the exchanges allowed participants to see that despite cultural nuances, people's lives were fundamentally similar across different European towns and cities. THE INTEGRATION OF WESTERN EUROPE AFTER THE SECOND WORLD WAR was driven by a broad movement aimed at peace, security and prosperity. Organized youth exchange between European cities formed an important part of that movement. This research focuses on young people who, from the 1960s onwards, participated in international exchanges organised by twinned towns, also called jumelage. Friends in a Cold Climate asks about the interactions between young people while taking into account the organisational structures on a municipal level, The project investigates the role of the ideology of a united West-Europe, individual desires for travel and freedom, the upcoming discourse about the Second World War and the influence of the prevalent “counterculture” of that period, thus shedding a light on the formative years of European integration. (2024-02-06) After the Second World War a number of friendship ties were established between towns in Europe. Citizens, council-officials and church representatives were looking for peace and prosperity in a still fragmented Europe. After a visit of the Royal Mens Choir Schiedam to Esslingen in 1963, representatives of Esslingen asked Schiedam to take part in friendly exchanges involving citizens and officials. The connections expanded and in 1970, in Esslingen, a circle of friends was established tying the towns Esslingen, Schiedam, Udine (IT) Velenje (SL) Vienne (F) and Neath together. Each town of this so called “Verbund der Ringpartnerstädte” had to keep in touch with at least 2 towns within the wider network. Friends in a Cold Climate looks primarily through the eyes the citizen-participant. Their motivation for taking part may vary. For example, is there a certain engagement with the European project? Did parents instil in their children a a message of fraternisation stemming from their experiences in WWII? Or did the participants only see youth exchange only as an opportunity for a trip to a foreign country? This latter motivation of taking part for other than Euro-idealistic reasons should however not be regarded as tourist or consumer-led behaviour. Following Michel de Certeau, Friends in a Cold Climate regards citizen-participants as a producers rather than as a consumers. A participant may "put to use" the Town Twinning facilities of travel and activities in his or her own way, regardless of the programme. The interviewee describes town twinning and youth exchanges that ranges from the period Yugoslavia was under leadership of Tito until the period when the Republic of Slovania became an independent country.

The datasets provided here were produced as part of the IMPREX project for work package 4, task 4 „Improving prediction on the climate scale“ and work package 9, task 3 “Case studies”. Analysis of the datasets are published in Deliverable 4.4 „Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale“ (Falloon et al 2019). The aim was to study the impact of internal climate model variability and bias correction method on the climate change signal of relevant flow indicators for the German waterways Rhine, Elbe and Danube.

To assess the impact of internal variability of the global climate model on future changes of flow, precipitation, temperature and global radiation of the 16-member ensemble generated with the RCM KNMI-RACMO2 driven by the GCM EC-EARTH 2.3 provided by WP3 of IMPREX were used. EC-EARTH was run 16 times from 1850 to 2100, each member starting from a slightly different initial state, under forcing of historical emissions until 2005 and the RCP8.5 greenhouse gas concentration pathway from 2006 onwards. Each of the EC-EARTH members was subsequently dynamically downscaled using KNMI-RACMO2 on a 0.11° (~12 km) resolved domain (Aalbers et al. 2018).

To correct the systematic model biases of climate models different bias correction methods were applied: (1) no bias correction, (2) linear scaling (Lenderink et al. 2007) and (3) quantile-quantile mapping (Piani et al. 2010). Bias correction relationships were derived for five-day periods (for each variable and location, in total 73 bias correction relationships were derived) including 13 days before and after the considered five-day period (total window size was 31 days) from the observations and values of the regional climate simulations. The period used to estimate the bias correction relationships was 1971-2000.

The hydrological model applied is called LARSIM-ME (ME – MittelEuropa = Central Europe) and is based in the model software LARSIM (Large Area Runoff SImulation Model) originally developed by Ludwig & Bremicker (2006). LARSIM-ME covers the catchments of the rivers Rhine, Elbe, Weser/Ems, Odra and Upper Danube. The total catchment size simulated by the model is approximately 800,000 km². The spatial resolution is 5 km x 5 km and the computational time-step is daily. As observed meteorological forcings, precipitation, air temperature and global radiation from the HYRAS data set (Rauthe et al. 2013) available for the 5 km x 5 km model grid and the period 1951-2015 were used. The hydrological model was calibrated using the automatic calibration scheme Shuffled Complex Evolution SCE-UA algorithm (Duan et al. 1994). For more details about the model see Meißner et al. (2017).

The meteorological variables air temperature, precipitation and global radiation produced by the KNMI RACMO-EC-EARTH 16 member ensemble (period 1951-2100) were interpolated to a 25 km x 25 km grid and afterwards bias corrected with respect to the observation data (HYRAS) used for calibration of the hydrological model LARSIM. From this 25 km x 25 km grid the bias corrected variables were downscaled to the 5 km x 5 km model grid of LARSIM using monthly background climatology fields on the 5 km x 5 km target grid of the HYRAS dataset. The bias-corrected and downscaled data was then used as meteorological forcing of LARSIM to calculate flow projections for the rivers Rhine, Elbe and Upper Danube (up to the German/Austrian border).

Dataset Q_OBS_DE.nc:

Mean daily observed flow of the gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe for the period 1951–2017 stored as variable q_obs(time=24472, stations=8).

Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG)

float q_obs(time=24472, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "observed streamflow"; :coordinates = "lat lon";

Dataset Q_HYRAS_LME.nc:

Mean daily simulated flow of the hydrological model LARSIM-ME forced by observed meteorology from the HYRAS dataset stored as variable q_sim (time=23741, stations=8). Period 1951-2015, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

float q_sim(time=23741, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "simulated streamflow"; :coordinates = "lat lon";

Q_RCP85_ECEARTH_RACMO_[bc]_LME.nc:

Mean daily projected flow of the hydrological model LARSIM-ME forced by 16 realizations of RCP8.5-ECEARTH-RACMO stored as variable q_sim(time=54787, realization=16, stations=8), first dimension time, second dimension realization and third dimension stations. Bias correction of meteorological forcings [bc]: NOBC: no bias correction, LS: linear scaling, QQMAP Quantile-Quantile Mapping. Period 1951-2100, Gauges Kaub, Koeln, Ruhrort / Rhine, Pfelling, Hofkirchen / Danube, Desden, Magdeburg Strombruecke, Neu-Darchau / Elbe.

float q_sim(time=54787, realization=16, stations=8); :units = "m3/s"; :_FillValue = -9999.0f; // float :long_name = "projected streamflow"; :coordinates = "lat lon";

Literature

Aalbers, E. E., G. Lenderink, E. van Meijgaard & B. J. J. M. van den Hurk (2018): Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50(11), 4745-4766

Duan, Q., S. Sorooshian & V. K. Gupta (1994): Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology 158(3–4), 265-284

Falloon, P., K. Williams, J. Andreu, A. Solera, S. Suárez-Almiñana, B. Klein, D. Meissner, J. Hunink, J. Eekhout & J. de Vente (2019): Estimation of hazards based on improved representation of highly vulnerable water resources of strategic importance on the climate scale. Deliverable 4.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, https://imprex.eu/system/files/generated/files/resource/imprex-deliverablereport-d4-4-final-1.pdf

Lenderink, G., A. Buishand & W. van Deursen (2007): Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences 11(3), 1143-1159

Ludwig, K. & M. Bremicker (2006): The Water Balance Model LARSIM –Design, Content and Applications. 22. C. Leibundgut, S. Demuth and J. Lange (Eds), Freiburger Schriften zur Hydrologie, Institut für Hydrologie, Universität Freiburg im Breisgau, Freiburg, 141 pp.

Meißner, D., B. Klein & M. Ionita (2017): Development of a monthly to seasonal forecast framework tailored to inland waterway transport in central Europe. Hydrol. Earth Syst. Sci. 21(12), 6401

Piani, C., J. O. Haerter & E. Coppola (2010): Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99(1-2), 187-192

Rauthe, M., H. Steiner, U. Riediger, A. Mazurkiewicz & A. Gratzki (2013): A Central European precipitation climatology - Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift 22(3), 235-256

The map shows the modeled average annual groundwater regeneration for the 30-year period 2021-2050 in the hydrological summer half-year (May-Oct.) in mm/a calculated with the “No climate protection” scenario (RCP8.5). Groundwater is a raw material that can regenerate and renew itself. The main supplier for the groundwater supply is precipitation water leaking in Lower Saxony. It ensures that the groundwater deposits of the storage rocks are replenished in the underground. The groundwater formation is particularly high in winter, as at this time a large part of the rainfall in the soil is leaking. In the warmer seasons, on the other hand, much of the precipitation already evaporates on the surface or is absorbed by plants. The new groundwater formation is widely distributed in different areas. It depends on the distribution of precipitation and evaporation, the characteristics of the soil, the land use (growth, degree of sealing), the relief of the land surface, the artificial drainage by drainage, the groundwater fluid level and the properties of the near-surface rocks. Since these parameters differ significantly in the smallest space in Lower Saxony, groundwater formation is also subject to large lateral fluctuations. In order to determine the new groundwater formation, there are different methods. The available maps show the area-differentiated designation of the mean groundwater formation, which was calculated using the mGROWA method (short for “monthly large-scale water balance”). The model mGROWA was developed for the large-scale simulation of the water balance at Forschungszentrum Jülich in cooperation with the LBEG (Herrmann et al. 2013) and updated methodically for Lower Saxony since 2016. In addition, a series of new input data has been used to provide an up-to-date data base for water management planning and water approval procedures. Daily and monthly climate projection data were used as climatic input data. The climate projection data represent the results of an ensemble of different climate models (the Lower Saxony climate ensemble AR5-NI v2.1 see Hajati et al. (2022)).The data was provided by the German Weather Service. The basis for this is the EURO-CORDEX Ensemble (Jacob et al., 2014). As part of the BMVI expert network, the DWD saw a downscale from a 12.5 km grid to a 5 km grid. The climate models are driven by the “No-Climate Protection” scenario (RCP8.5).This is a scenario of the IPCC, which describes a continuous increase in global greenhouse gas emissions, resulting in an additional radiative propulsion of 8.5 watts per m² compared to pre-industrial levels by the end of the 21st century. The results of all climate models are equally likely. Therefore, in addition to the mean, which shows a tendency, the upper (maximum) and lower (minimum) edge of the result bandwidth can be retrieved via the maptip. For better regionalisation, the climatic input parameters precipitation and potential evaporation with bilinear interpolation were scaled down to a 500 x 500 m grid for mGROWA22. Groundwater is a raw material that can regenerate and renew itself. The main supplier for the groundwater supply is precipitation water leaking in Lower Saxony. It ensures that the groundwater deposits of the storage rocks are replenished in the underground.The groundwater formation is particularly high in winter, as at this time a large part of the rainfall in the soil is leaking. In the warmer seasons, on the other hand, much of the precipitation already evaporates on the surface or is absorbed by plants. The new groundwater formation is widely distributed in different areas. It depends on the distribution of precipitation and evaporation, the characteristics of the soil, the land use (growth, degree of sealing), the relief of the land surface, the artificial drainage by drainage, the groundwater fluid level and the properties of the near-surface rocks. Since these parameters differ significantly in the smallest space in Lower Saxony, groundwater formation is also subject to large lateral fluctuations.

In order to determine the new groundwater formation, there are different methods. The available maps show the area-differentiated designation of the mean groundwater formation, which was calculated using the mGROWA method (short for “monthly large-scale water balance”). The model mGROWA was developed for the large-scale simulation of the water balance at Forschungszentrum Jülich in cooperation with the LBEG (Herrmann et al. 2013) and updated methodically for Lower Saxony since 2016. In addition, a series of new input data has been used to provide an up-to-date data base for water management planning and water approval procedures.

Daily and monthly climate projection data were used as climatic input data. The climate projection data represent the results of an ensemble of different climate models (the Lower Saxony climate ensemble AR5-NI v2.1 see Hajati et al. (2022)). The data was provided by the German Weather Service. The basis for this is the EURO-CORDEX Ensemble (Jacob et al., 2014). As part of the BMVI expert network, the DWD saw a downscale from a 12.5 km grid to a 5 km grid.

The climate models are driven by the “No-Climate Protection” scenario (RCP8.5). This is a scenario of the IPCC, which describes a continuous increase in global greenhouse gas emissions, resulting in an additional radiative propulsion of 8.5 watts per m² compared to pre-industrial levels by the end of the 21st century.

The results of all climate models are equally likely. Therefore, in addition to the mean, which shows a tendency, the upper (maximum) and lower (minimum) edge of the result bandwidth can be retrieved via the maptip.

For better regionalisation, the climatic input parameters precipitation and potential evaporation with bilinear interpolation were scaled down to a 500 x 500 m grid for mGROWA22.

In January 2025, Germany experienced an overall average of 61 sunshine hours, which was an increase compared to the previous month, despite it being winter. Sunshine hours are also referred to as sunshine duration. As can be seen on this graph, the amount that Germany receives differs by season, even quite starkly just by month. Sunniest states When looking at federal states in Germany in 2024, the sunniest states in summer were Berlin, Brandenburg and Saxony. Confirming popular opinion, Hamburg was indeed the state with less sunshine hours in recent years, though not the least sunny compared to others further down the list. In winter, based on recent figures, Germany counted 392 sunshine hours. These figures may change more in the coming years due to the effects of climate change on the weather all over the country. National weather service The German National Meteorological Service (Deutscher Wetterdienst or DWD) monitors the weather in Germany. The service is a federal authority providing information for the population and conducting scientific research. It is also responsible for issuing official warnings when weather conditions are predicted to be threatening.

In January 2025, the average temperature in Berlin was 2.8 degrees Celsius, this was lower than in December 2024. However, it was a significant increase in temperature compared to the January a year ago.

This statistic displays the average monthly rainfall in Germany over the past 20 years. It shows that over the past twenty years the month with the highest average rainfall has been June, with an average rainfall of 69.4 mm. On average, March has been the driest month.

On July 14, 2021, the German Weather Service measuring station in Hagen-Holthausen recorded a rainfall of 241.3 liters per square meter within 22 hours. That day, several German states were experiencing ongoing heavy rainfall at extreme levels, which led to the worst flooding disaster in the country's recent history.

It is estimated that by the year 2050, the temperature of the warmest month in the German capital of Berlin will have increased by 6.1 degrees Celsius. In the south of the country, Munich is expected to experience an increase of just under five degrees Celsius.