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 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.

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.

Forecast: Average per Capita Monthly Mobile Data Use in Germany 2024 - 2028 Discover more data with ReportLinker!

Forecast: Mobile Telephony Average Monthly Revenue per SIM-card in Germany 2022 - 2026 Discover more data with ReportLinker!

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.

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.

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.

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.

Forecast: Average Monthly Fixed Broadband Data per User in Germany 2022 - 2026 Discover more data with ReportLinker!

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.

Forecast: Average Number of Rooms Per Person in Cities in Germany 2024 - 2028 Discover more data with ReportLinker!

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.

• Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.
• Average, minimum and maximum 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 `FRCHEM_2021_AirTemperature_MetaData.txt`.

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

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).

Ambios provides trusted, real-time, historical Air Quality Data from a decentralized network of 3,000+ outdoor sensors across 20+ countries. Our data includes key environmental variables:

-PM1, PM2.5, PM10 (particulate matter) -NO₂, O₃, CO (gaseous pollutants) -Temperature & Humidity

This high-frequency, hyperlocal dataset is used across industries for operational, regulatory, and research purposes.

Use Cases Include:

-Smart Cities: Monitor pollution hotspots, evaluate clean air zones, and drive zoning or mobility policy. -Real Estate & ESG: Support green certifications, assess site-level environmental quality, and meet reporting standards. -Logistics & Transport: Optimize routes, reduce emissions, and manage compliance in urban corridors. -Government & Regulation: Fill gaps in national monitoring networks, inform alerts, and shape environmental policy. -Research & Academia: Power climate, health, and pollution exposure studies with real-world environmental data.

Ambios data is 100% first-party, verifiable, and available in real-time or historical formats. Our system is built on a DePIN (Decentralized Physical Infrastructure Network) and ensures transparency, traceability, and global scalability.

Whether you’re building environmental models, managing urban systems, or meeting ESG goals, Ambios Air Quality Data provides the environmental intelligence you need to act.

Seasonal animal migration is a widespread phenomenon. At the species level, it has been shown that many migratory animal species track similar climatic conditions throughout the year. However, it remains unclear whether such niche tracking pattern is a direct consequence of individual behaviour or emerges at the population or species level through behavioural variability. Here, we estimated seasonal niche overlap and seasonal niche tracking at the individual and population level of Central European White Storks (Ciconia ciconia). We quantified niche tracking for both weather and climate conditions to control for the different spatio-temporal scales over which ecological processes may operate. Our results indicate that niche tracking is a bottom-up process. Individuals mainly track weather conditions while climatic niche tracking mainly emerges at the population level. This result may be partially explained by a high degree of intra- and inter-individual variation in niche overlap between seasons. Understanding how migratory individuals, populations and species respond to seasonal environments is key for anticipating the impacts of global environmental changes. Methods We trapped 62 adult white storks in the state of Saxony-Anhalt, Germany, and equipped them with solar GPS-ACC transmitters (e-obs GmbH; Munich, Germany) that weighed 55 g including harness, ca. 2% of the average stork’s weight (see [25]). The transmitters recorded GPS fixes every 5 min when solar conditions were good (95% of the time) or every 20 min otherwise. This dataset include a set of maximum 100 GPS locations randomly selected per day and individual to estimate the seasonal niche and to avoid over-fitting the data to some locations.

In addition, each datapoint were associated to three type of environmental variables at two scales, weather and climate. For weather variables, the datapoints were annotated with environmental data of temperature (Land Surface Temperature & Emissivity 1-km Daily Terra; MOD11A1 V6), precipitation (ECMWF Interim Full Daily SFC-FC Total Precipitation; 0.75 deg.; 3 hourly) and Normalized Difference Vegetation Index (NDVI; MODIS Land Vegetation Indices 1km 16 days Terra) using the Env-DATA track annotation tool of MoveBank. For the climate data, we used long-term averaged monthly temperature and precipitation patterns for the time period 1979-2013 at 1 km resolution (CHELSA), and monthly NDVI for the time period 1982-2000 (GIMMS AVHHR Global NDVI), and extracted the values of each variable for all selected points using the “raster” package.

Forecast: Average Monthly Mobile Voice Calls per SIM-card in Germany 2022 - 2026 Discover more data with ReportLinker!

Forecast: Average Number of Rooms Per Person in All Populated Areas in Germany 2024 - 2028 Discover more data with ReportLinker!