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.

Temperature in Germany increased to 11.19 celsius in 2024 from 10.89 celsius in 2023. This dataset includes a chart with historical data for Germany Average Temperature.

This data shows the average temperature in Germany 2024, by federal state. That year, the average temperature in the city-state Berlin was **** degrees Celsius.

In 2024, Germany recorded a mean temperature of **** degrees Celsius. This was practically unchanged compared to the year before. Figures fluctuated during the timeline presented, but have grown compared to the 1960s and 70s.

In 2024, the average summer temperature in Germany was **** 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 **** 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.

In 2023/2024, the average winter temperature in Germany was *** 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 **** days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

In 2024, the average autumn temperature in Germany was 10.5 degrees Celsius. This was a decrease from the previous year, when the average temperature in autumn was around 11.5 degrees Celsius. This statistic shows the average autumn temperature in Germany from 1960 to 2024.

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

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:

During the specified timeline, 2024 was the warmest year recorded in Germany, with an average temperature of 10.9 degrees Celsius. The average temperature tended to rise over the timeline under review. The statistic presents the years with the highest average temperature in the country between 1934 and 2024.

In June 2025, the average temperature in Berlin was **** degrees Celsius. This was an increase compared to the June a year ago.

In 2025, the average spring temperature in Germany was measured at *** degrees Celsius. This shows a decrease ****degree Celsius compared to 2024. The highest average spring temperature saw the year of 2024.

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10, 79098 Freiburg im Breisgau, Germany.

• Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2024.
• Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.
• Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).
• Detailed information on gap-filled data is provided.
• Note: All times are provided in UTC, not local time.

For more details read `FRWRTM_2024_AirTemperature_MetaData.txt`.

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

Average temperatures (tvs) 1969-2019 — Saarland — for the forest vegetation period May to September divided into heat levels. Calculated on a grid of 1x1km of a digital elevation model based on the station measurement network of the DWD (Climate Data Center (CDC), grid of the monthly mean of the air temperature (2 m) for Germany, versionv1.0). Climate and weather-influencing processes (e.g. urban heat island, cold air discharge) that are not recorded directly with the station measurement network or cannot be determined by the regression method are not shown in the grid data. Data preparation by the Rhineland-Palatinate Competence Center for Climate Change Impacts, compilation by SaarForst Landesbetrieb.

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 **** degrees Celsius. On average, January has been the coldest month.

A total of 54 Geotiffs in EPSG:4326 (can easily be opened with GIS software such as ArcGIS or QGIS) is provided . These maps are the results of 18 scenarios (S01-S18) proposed to evaluate technical requirements of electricity self-sufficient single family houses in low population density areas in Germany and the Czech Republic. The non-data values inside of the territory of the countries correspond either to pixels with no population or population beyond 1,500 inhabitants per square kilometre (The classification was made using population data from the LUISA project of the Joint Research Centre of the European Commission). The file names can be interpreted in the same way as the following example: S01_Battery_min_cost_no_sc.tif where S01 is the scenario number (01 to 18 are possible), Battery is the type of technology presented in the map (there are also optimally tilted photovoltaic panels named "PV1" and photovoltaic panels with 70° inclination named "PV2"), “min” stands for minimizing and the following word stands for the minimization objective. In this case with “cost” the objective of the scenario is to minimize cost (“battery” for battery size and “pv” for photovoltaic size are also possible). Additionally, there is “no_sc” for case studies that do not consider snow cover and "sc" in case snow cover is considered. Finally some of the files include a year at the end of the file name. This stands for the year of the irradiation and temperature data sets that were used to run the scenario. All files without a year correspond to scenarios calculated with average weather data (Average hours calculated from two decades of data from the COSMO-REA6 regional reanalysis).

In addition to telecommunications, rail transport and gas and energy supply, water supply is the fourth major infrastructure sector characterised by a supply network owned by the industry players. However, in contrast to the communications market and, in particular, the gas and energy supply sector, there is no networked infrastructure in the water supply sector whose technology allows for nationwide transmission. Only in some regions of Germany do long-distance water suppliers operate thanks to favourable geographical conditions and for historical reasons. Industry turnover is expected to increase by 2.5% year-on-year to € 19.5 billion in 2025. This corresponds to average sales growth of 4% per year since 2020. The strong growth in 2022 had a significant impact on sales development after sales had fallen in previous years due to the coronavirus pandemic and the decline in production volumes. High average temperatures and precipitation levels in 2023 and 2024 also benefited water suppliers. Due to high energy costs, the profit margin for water suppliers remains low in the current year.The turnover of water suppliers is largely determined by the supply requirements of commercial, private and public customers, which in turn depend heavily on weather conditions and the volume of water pumped. Following the droughts in the summer months of 2021 and 2022, groundwater supplies are expected to rise in many areas of Germany in the current year, despite average annual temperatures remaining high, which should have a positive impact on the industry. On the other hand, persistently high consumer prices for water are likely to lead to savings measures and a decline in household demand for water.In the next five years, ever stricter water quality standards, stagnating population figures and increasing water conservation measures among the population are likely to lead to a slight decline in supply requirements from private and commercial customers. As a result, industry turnover will continue to grow, but not to the same extent as in the last five-year period. Industry sales are expected to increase by an average of 2.6% per year to EUR 22.2 billion in 2030. At the same time, the reduction in the workforce made possible by advancing digitalisation and automation is likely to continue, which should stabilise the industry's profitability.

We related the sea surface temperature data from the Helgoland Roads Time Series, one of the most important and detailed long-term in situ marine ecological time series, to the Sylt Roads Time Series and spatially averaged North Sea, Germany, Europe, North Atlantic and Northern Hemisphere surface temperatures. The hierarchical and comparative statistical evaluation of all of these time series relative to one another allows us to relate marine ecosystem change to temperature in terms of time (from 1962 to 2019) and spatial scales (global to local). The objectives are:1.to investigate the warming in the North Sea in terms of different geographical scales and typical weather indices (North Atlantic Oscillation),2.to document the different types of changes observed: trends, anomalies and variability3.to differentiate seasonal shifts,4.to evaluate anomalies and frequency distributions of temperature over time, and5.to evaluate hot and cold spells and their variability.Spatially averaged datasets are extracted from gridded HadCRUT4 and HadSST3 reanalysis, the European Environment Agency and the German Weather Service (DWD). Datasets are analyzed in terms of yearly and monthly surface temperature averages and their anomalies relative to 1960s-1990s period.The North Atlantic Oscillation winter mean is the December, January and February average of the data made available by the National Center for Atmospheric Research (NCAR).For detailed information about the datasets, please refer to Amorim & Wiltshire et al. (2023) - https://doi.org/10.1016/j.pocean.2023.103080.

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.