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.

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.

In winter 2024/25, the average temperature in Bremen was *** degrees Celsius. This made it the warmest federal state during this timeline, followed by Schleswig-Holstein. The coldest at the same time was Bavaria, with an average temperature of *** degrees Celsius.

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.

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

This dataset contains outputs from two runs of a coupled atmosphere-ocean model at DKRZ in Hamburg. The runs were made in 1990 and they include a control run and an IPCC Scenario A run. We received 100 years of monthly 10-year climatologies of 2m temperature, precipitation, net surface solar radiation, and reflected surface solar radiation in GRIB0 format. We also received outputs from 100-year transient runs (control, IPCC Scenario A, and IPCC Scenario D). These included monthly means of 59 parameters at the surface and 15 isobaric levels. We were notified in May 1993 that there was a problem with the vertical interpolation in those runs, so the data are no longer in our public distribution, but they remain in our archive.

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.

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:

Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without. TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023). A description of the methods used can be found in: Dietrich, H.; Wolf, T.; Kawohl, T.; Wehberg, J.; Kändler, G.; Mette, T.; & Röder, A. & Böhner, J. (2019). Temporal and Spatial High-Resolution Climate Data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science 76, 6. https://doi.org/10.1007/s13595-018-0788-5 Kawohl, T.; Dietrich, H.; Wehberg, J.; Böhner, J.; Wolf, T. & Röder, A. (2017). Das Klima in 80 Jahren – Wein- statt Waldbau? – AFZ-Der Wald 15: 32-35. For GIS-based Terrain-parameterization methods and their application in statistical-dynamical downscaling see, e.g.: Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., & Böhner, J. (2015). System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015. Böhner, J. & Bechtel, B. (2018): GIS in Climatology and Meteorology. – In: Huang, B. [Ed.]: Comprehensive Geographic Information Systems. – Vol. 2, pp. 196–235. Oxford: Elsevier. http://dx.doi.org/10.1016/B978-0-12-409548-9.09633-0. Quality: -- Units: MJ/m2; MJ/m2; mm; hPa; degC; degC; degC; m/s GeoLocation: westBoundCoordinate: 278750; westBoundCoordinateUnit: m; eastBoundCoordinate: 923000; eastBoundCoordinateUnit: m; southBoundCoordinate: 5234000; southBoundCoordinateUnit: m; northBoundCoordinate: 6102750; northBoundCoordinateUnit: m; ProjectCoordinateSystem: Transverse_Mercator; ProjectionCoordinateSystemParameters: [+proj=utm +datum=WGS84 +zone=32 +no_defs]. geoLocationPlace:Germany; UTMZone: 32 Size: Files are first packed into zip-archives and then further grouped together into one tar-archive per variable and 10-year period. The original file size is between about 4 and 7.5 GB per year and variable. The file size of the tar archives ranges between 3 GB and 70 GB. Format: SAGA-Grid (.sgrd), https://saga-gis.sourceforge.io/en/index.html DataSources: DWD Climate Data Center (CDC): Historical daily station observations (temperature, pressure, precipitation,sunshine duration, etc.) for Germany, version v21.3, 2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-kl-historical and DWD Climate Data Center (CDC): Historical daily precipitation observations for Germany, version v21.3,2021. Dataset-ID: urn:x-wmo:md:de.dwd.cdc::obsgermany-climate-daily-more_precip-historical. http://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/ Contact: Prof. Dr. Jürgen Böhner, Universität Hamburg, Center for Earth System Research and Sustainability, Institute of Geography, Bundesstraße 55, 20146 Hamburg, juergen.boehner (at) uni-hamburg.de; https://www.geo.uni-hamburg.de/en/geographie/mitarbeiterverzeichnis/boehner.html Webpage: https://www.waldklimafonds.de/ and https://www.lwf.bayern.de/boden-klima/wasserhaushalt/223446/index.php Created within project "Veränderte Produktivität und Kohlenstoffspeicherung der Wälder Deutschlands angesichts des Klimawandels" (WP-KS-KW, https://www.waldklimafonds.de/index.php?id=13913&fkz=22WC400312) funded by the Federal Ministry of Food and Agriculture (BMEL). The data update until 2020 was performed in the project "Wasserhaushalt im Klimawandel" (WHH-KW, https://www.fnr.de/index.php?id=11150&fkz=22WK414104) funded by the Federal Ministry of Food and Agriculture (BMEL) and the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) within the framework of the Forest Climate Fund.

The grids were derived from data from the DWD stations and qualitatively equivalent partner network stations in Germany, taking into account the altitude dependencies. pdf

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`.

Regular monitoring

This document describes publicly available urban climate data from the DWD Climate Data Center (CDC). The measurements are taken in the center of large cities in order to be able to compare the city and the surrounding area and to document the influence of different urban structures on the meteorological parameters. Due to their urban location, the stations of the special urban climate measurement network cannot meet the usual World Meteorological Organization (WMO) standards, but follow the recommendations of the WMO Instruments and observing methods report no. 81 “Initial guidance to obtain representative meteorological observations at urban sites” (Oke, 2006). The data in the recent/ directory is preliminary data (not yet fully quality checked). Further information: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate_urban/hourly/air_temperature/recent/BESCHREIBUNG_obsgermany_climate_urban_hourly_tu_recent_de.pdf

GTS data from Germany for 2007,
data are extracted from the original WMO bulletins for a subset of WMO FM12 code,
data have been collected and processed at the Department of Meteorology and Geophysics,
no data quality control at the Department of Meteorology and Geophysics, University of Vienna at all,
3h-accumulation performed at the University of Hohenheim and University of Vienna,
for further details see file: jdc_data_description.pdf in entry "jdc_obsdata_info_1".

DE: Annual Surface Temperature: Change Since 1951 1980 data was reported at 1.304 Number in 2021. This records a decrease from the previous number of 2.499 Number for 2020. DE: Annual Surface Temperature: Change Since 1951 1980 data is updated yearly, averaging 1.302 Number from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 2.506 Number in 2014 and a record low of -0.706 Number in 1996. DE: Annual Surface Temperature: Change Since 1951 1980 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Germany – Table DE.OECD.GGI: Environmental: Climate Risk: OECD Member: Annual.

Abstract: Gridded climate time series for Germany derived through downscaling of EURO-CORDEX historical simulations and climate projections from following ensemble members (www.euro-cordex.net):: MPI-M-MPI-ESM-LR(r1)_CLMcom-CCLM4-8-17: RCPs 8.5, 4.5, 2.6 and historical (MPI_CLM) ICHEC-EC-EARTH(r12)_KNMI-RACMO22E(v1): RCP 8.5 and historical (ECE_RAC) CCCmaCanESM2_r1i1p1_CLMcomCCLM4817_v1: RCP 8.5 and historical (CA2_CLM) All time series were consistently calculated at daily resolution and a grid cell spacing of 250 × 250 meter. Historical 1950–2005 data sets and 2006–2100 RCP projections comprise of mean temperature, minimum temperature, maximum temperature, precipitation, global radiation, air pressure, wind speed, specific humidity and delineated variables (relative humidity, potential evapotranspiration, water vapor pressure). All data sets except specific humidity and surface air pressure are available twice, as downscaled but non-bias corrected EURO-CORDEX data, and as bias corrected data sets. Correction terms for empirical adjustment of downscaling results were computed according to Sachindra et al. (2014) using gridded WP-KS-KW data as observational reference (Dietrich et al. 2019). Dietrich, H., Wolf, T., Kawohl, T., Wehberg, J., Kändler, G., Mette, T., Röder, A. & Böhner, J. (2019): Temporal and spatial high-resolution climate data from 1961-2100 for the German National Forest Inventory (NFI). – Annals of Forest Science 76: 6, https://doi.org/10.1007/s13595-018-0788-5. Sachindra, D.A., Huang, F., Bartona, A. & Pereraa, B.J.C. (2014): Statistical downscaling of general circulation model outputs to precipitation – part 2: bias-correction and future projections. – Int. J. Climatol. 34: 3282–3303, https://doi.org/10.1002/joc.3915. TableOfContents: daily mean 2m-air temperature (tav); daily minimum 2m-air temperature (tmn), daily maximum 2m-air temperature (tmx); daily sum of precipitation (prz); daily sum of global radiation (sgz); daily surface air pressure (psz); daily mean 10m wind speed (wsp); daily mean specific humidity (hus); daily mean relative humidity (rhm); potential evapotranspiration (pet); daily mean water vapor pressure (vap) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 56; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2100-12-31 23:59:59; temporalDuration_RCPs: 95; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none

This is the raw data that was used as input to create the German test reference years (2017). The departmental research project "TRY advancement" was financed by the BBSR via the research initiative Future Construction

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.

The Passow 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 Passow was installed in 2011. It is located within a field, next to some drainage installations. 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_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.272 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.