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.

This dataset, provides detailed weather and climate statistics for major cities in Germany from 2015 to 2023.

It includes rainfall amounts, temperatures, humidity levels, and other geographical and climatic details, making it ideal for analyzing weather patterns, climate change, and their impacts across different regions.

1. City: Name of the city.

2. Latitude: City's latitude in degrees.

3. Longitude: City's longitude in degrees.

4. Month: The month number (1-12).

5. Year: The year of the data.

6. Rainfall (mm): Rainfall amount in millimeters.

7. Elevation (m): City’s elevation above sea level in meters.

8. Climate_Type: The climate classification of the city.

9. Temperature (°C): Average temperature for the month in Celsius.

10. Humidity (%): Average humidity level for the month in percentage.

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.

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 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.

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:

Data sets of current German weather stations updated hourly or every twelve hours. Data sets, in German, include: * Daily mean values ??of temperature, updated hourly. Daily archive since 29.1.2008 * Daily maximum and minimum temperature, updated every 12 hours. Daily archive since 21.7.2008 * Monthly mean values ??of temperature and deviation, updated daily . * Rainfall in the last 12 hours and monthly total, updated every 12 hours . * Monthly totals of precipitation and relative to langj. means in%, updated every 12 hours. Monthly Archive since Feb. 2008 * Air pressure and pressure tendency, updated hourly.

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

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

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

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 **** mm. On average, March has been the driest month.

Germany saw a decrease of precipitation by around ***** millimeters in 2024, compared to the average from 1981 to 2010. Figures varied during the timeline.

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.

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.

The historical monthly degree days are calculated from publicly available station data from the DWD Climate Data Center (CDC). The monthly degree days according to VDI 3807 are the sums of the degree days over a calendar month. The degree days refer to a room temperature of 20 degrees Celsius. Degree days are calculated as the temperature difference between room temperature and the daily mean temperature (degrees Celsius). Only the days are counted when the daily average outside temperature is less than 15 degrees Celsius (heating day).
Further information: https://opendata.dwd.de/climate_environment/CDC/derived_germany/techn/monthly/heating_degreedays /hdd_3807/historical/BESCHREIBUNG_derivgermany_techn_monthly_heating_degreedays_hdd_3807_historical_de.pdf

Temperature is foundational for understanding climate dynamics, human comfort, building performance, and risk forecasting. For ESG reporting, precision agriculture, or infrastructure monitoring, accurate and hyperlocal temperature data is essential. Ambios provides real-time and historical Temperature Data collected from over 3,000+ first-party sensors in 20 countries. With high spatial and temporal resolution, our decentralized environmental network delivers reliable temperature insights for various applications.

-3,000+ first-party sensors delivering data every 15 minutes -Coverage across 20 countries and diverse climates -Historical data available -Designed for integration into ESG reports, digital twins, and risk dashboards -Supports smart infrastructure, crop modeling, heat resilience, and HVAC optimization

Use cases include:

-ESG disclosures and climate-related risk tracking -Smart building temperature control and energy savings -Agricultural yield optimization and weather-responsive irrigation -Urban heat island analysis and resilience planning -Scientific research and real-time environmental modeling

Backed by DePIN (Decentralized Physical Infrastructure Network) infrastructure, Ambios ensures the data is trustworthy, tamper-proof, and scalable—giving enterprises, cities, and developers the foundation to build intelligent, climate-resilient systems. From field to cloud, Ambios Temperature Data delivers the accuracy, resolution, and transparency needed for today’s environmental and operational demands.

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

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.

• Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.
• 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_2019_AirTemperature_MetaData.txt`.

Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Germany DE: Official Development Assistance: % of Total ODA: Climate Change Adaptation data was reported at 24.570 % in 2021. This records an increase from the previous number of 22.180 % for 2020. Germany DE: Official Development Assistance: % of Total ODA: Climate Change Adaptation data is updated yearly, averaging 22.500 % from Dec 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 29.570 % in 2019 and a record low of 6.210 % in 2010. Germany DE: Official Development Assistance: % of Total ODA: Climate Change Adaptation 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: Environmental Policy, Taxes and Transfers: OECD Member: Annual.

Germany Cooling Degree Days data was reported at 240.510 Degrees Celsius in 2020. This records a decrease from the previous number of 325.790 Degrees Celsius for 2019. Germany Cooling Degree Days data is updated yearly, averaging 160.700 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 402.810 Degrees Celsius in 2018 and a record low of 54.900 Degrees Celsius in 1977. Germany Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Climate Risk. A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;