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 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 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.

Daily data averaged across Germany for the period of 2016-01-01 till 2021-06-27:

temperature_mean: mean daily temperature in degree Celsius averaged across all weather stations in Germany.

temperature_max: maximum daily temperature in degree Celsius averaged across all weather stations in Germany.

precipitation: daily precipitation sum in millimeter (equals liter per square meter) averaged across all weather stations in Germany.

sunshine: sunshine duration per day averaged across all weather stations in Germany.

gemittelte Werte basierende auf Daten des Deutschen Wetterdiensts, Vermessungsverwaltungen der Länder und BKG (https://gdz.bkg.bund.de/)

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.

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.

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

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.

Historical weather data is essential for understanding environmental trends, assessing climate risk, and building predictive models for infrastructure, agriculture, and sustainability initiatives. Among all variables, temperature and humidity serve as core indicators of environmental change and operational risk.

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The grids were derived from data from the DWD stations and qualitatively equivalent partner network stations in Germany.

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 variability 3.to differentiate seasonal shifts, 4.to evaluate anomalies and frequency distributions of temperature over time, and 5.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 German Weather Service (DWD) maintains ground based network of different instruments. One of these is the operational C-band Radar network. SAMD provides the data of the high level product RADOLAN, reflectivity and precipitation data on a regular grid, with 1x1km² horizontal and 5 minutes temporal resolution. Furthermore DWD provides cloud bottom heights from the ceilometer. The GFZ German Research Centre for Geosciences in Potsdam operates a German wide ground-based GNSS atmosphere sounding network. GNSS stands for Global Navigation Satellite Systems. One of the products is the path of integrated water vapor, provided in SAMD.

The German Weather Service (DWD) maintains ground based network of different instruments. One of these is the operational C-band Radar network. University of Bonn provides the data of the high level product RADOLAN, reflectivity and precipitation data on a regular grid, with 1x1km² horizontal and 5 minutes temporal resolution.

To meet the goal of the Federal Fertilizer Ordinance (DüV) of May 26, 2017 (Federal Law Gazette I p. 1305), last amended on August 10, 2021 (Federal Law Gazette I p. 3436), which requires the resource-saving use of plant nutrients and the fulfillment of the requirements of water protection, the first ordinance to amend the Thuringian Fertilizer Ordinance (ThürDüV) came into force on November 30th, 2022. In Thuringia, an area has been designated to protect water bodies from nitrate pollution (nitrate setting) in order to reduce the nutrient input from agriculture in these polluted areas. According to § 13a Para. 2 No. 7 DüV, fertilizers with a significant nitrogen content may be used in these areas in the case of cultivation of crops with sowing or planting after February 1st. only applied if a catch crop was grown on the affected area in autumn of the previous year and not before 15.01. was wrapped. This does not apply to areas on which crops were planted after 01.10. are harvested and areas in areas where the long-term average annual precipitation is less than 550 mm. The designated agricultural areas with a long-term average annual precipitation of less than 550 mm are the reference plots in accordance with the Thuringian ordinance for the implementation of the common agricultural policy in the currently valid version, which are identified by the field block. Agricultural areas that are at least half of their area in the area designated by the German Weather Service form the areas with long-term average annual precipitation of less than 550 mm. The areas are based on the provision of the 30-year mean (1991-2020) for long-term mean annual precipitation of less than 550 mm. These are expected to remain valid for the current decade. The designation of this area setting is linked to the nitrate setting and is only specified for the affected field blocks. The geodata of the affected reference plots are updated annually on February 1st. calculated and published in digital form in the Geoportal Thuringia.

Abstract: This data set contains time series of air pressure, precipitation sum, wind speed, wind direction, air temperature, and relative humidiy, measured at the synoptic Lindenberg weather station (10393) during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May to August 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). Data are level-1 data as 10-minute averages (sums) based on 1 Hz sampling organized in daily files. This data set further contains time series of the downward surface radiation flux densities (short-/longwave irradiance) measured at the radiation platform in Lindenberg during FESSTVaL from May to August 2021. Data are level-1 data as 1-minute averages based on 1 Hz sampling organized in daily files.

TableOfContents:

• Basic Meteorological Data: rainfall amount; rainfall amount quality flag; air pressure; air pressure quality flag; air temperature; air temperature quality flag; relative humidity; relative humidity quality flag; wind speed; wind speed quality flag; wind from direction; wind from direction quality flag
• Radiation Data: global irradiance at the surface; global irradiance at the surface standard deviation; global irradiance at the surface quality flag; diffuse irradiance at the surface; diffuse irradiance at the surface standard deviation; diffuse irradiance at the surface quality flag; direct irradiance at the surface; direct irradiance at the surface standard deviation; direct irradiance at the surface quality flag; long-wave irradiance at the surface; long-wave irradiance at the surface standard deviation; long-wave irradiance at the surface quality flag; solar zenith angle

Technical Info:

• Basic Meteorological Data: dimension: 144 x 1; temporalExtent_startDate: 2021-05-01 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: rain[e] H3, PTB220, LTS2000, EE33 DWD, 2D-ultrasonic anemometer, LAM630; instrumentType: weighing tipping bucket, capacitive digital barometer, platinum resistance thermometer, heated capacitive hygrometer, 2D-ultrasonic anemometer, sensor shield; instrumentLocation: all Lindenberg synoptic weather station; instrumentProvider: Lambrecht GmbH, Vaisala Oy, Vaisala Oy, e+e Elektronik GmbH, Thies GmbH, Eigenbrodt GmbH
• Radiation Data: dimension: 1440 x 1; temporalExtent_startDate: 2021-05-01 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 1; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 0; horizontalEndUnit: meters; instrumentNames: CMP22, CMP22, CH1, CGR4; instrumentType: ventilated and heated pyranometer, shaded ventilated and heated pyranometer on solar tracker, ventilated pyrheliometer on solar tracker, shaded ventilated and heated pyrgeometer on solar tracker; instrumentLocation: all Lindenberg radiation platform; instrumentProvider: all Kipp&Zonen B.V.

Methods:

• Basic Meteorological Data: Data undergo standard quality checks implemented in the DWD synoptic station network. This includes range tests, plausibility tests with respect to neighbouring stations and to temporal changes. For temperature, a second sensor is operated for comparison. Each measured value is accompanied by a quality flag where 0 = data value missing, 1 = good quality, 2 = interpolated or gap-filled by data from an alternative sensor, 3 = dubious quality, 4 = bad quality, 9 = no quality information available. The wind measurements are performed at the top of a hill at the observatory site, the measurement place is surrounded by forest edges at distances of a few decametres to about 100 metres except for winds from SSW to NW, they cannot be considered as representative.
• Radiation Data: Radiation flux sensors are operated in ventilated shields. The uncertainty in the observational period (given as 95 % confidence intervals) is estimated from internal comparisons at ± 4.5 W/m² (or 2.5 %), ± 5 W/m² (or 1.5 %), and ± 6.5 W/m² (or 2 %) for the diffuse, global and direct component, respectively. The longwave uncertainty is less than ± 5 W/m². In situ calibrations were frequently conducted during the observational period using reference sensors directly traceable to the World Radiometric Reference (WRR) and the World Infrared Standard Group (WISG) for shortwave and longwave radiation, respectively. Quality control follows the recommendations of the WMO baseline surface radiation network (BSRN). It includes absolute value range tests and inter-comparison versus a second independent radiation flux measurement at the same site. The temperature of the emitting sensor surface of the pyrgeometer is checked for plausibility vs. ambient air temperature. Standard deviations are given for all variables listed below. Each measured value is accompanied by a quality flag where 0 = valid, 2 = invalid, 5 = value between extremely rare limits and physically possible limits, 6 = value out of physically possible limits, 9 = value missing.

Units: (see TableOfContents)

• Basic Meteorological Data: kg m-2;1;pa;1;K;1;1;1;m s-1;1;degrees;1
• Radiation Data: W m-2;W m-2;1;W m-2;W m-2;1;W m-2;W m-2;1;W m-2;W m-2;1;degrees

geoLocations:

• BoundingBox: westBoundLongitude: 14.118 degrees East; eastBoundLongitude: 14.1220 degrees East; southBoundLatidude: 52.208 degrees North; northBoundLatitude: 52.209 degrees North; geoLocationPlace: Germany, UTM zone 33U
• Locations:
  • Basic meteorological data: 52.118 °N, 14.120 °E, 98 m to 125 m above mean sea level, 1 m to 10.4 m above ground
  • Radiation data: 52.208 °N, 14.122 °E, 125 m above mean sea level, 1.7 m to 1.9 m above ground

Size: Data (level 1 only) are packed into two compressed tar-archives. Their sizes are 0.9 Mbyte for the basic meteorological data and 3.9Mbyte for the radiation data.

Format: netCDF

DataSources: Single site ground-based instrument measurements, see "Technical Info" for instruments

Contact: claudia.becker (at) dwd.de; stefan.wacker (at) dwd.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval.html

see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html

Oberstdorf, located in Bavaria, recorded around ***** liters of precipitation per square meter in 2024. Kiel in the north, on the other hand, recorded annual precipitation of around *** liters per square meter.