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.

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.

The dataset provides official temperature data measured from 513 weather stations in Germany from 1990 to 2021.

The original data are provided by the German Meteorological Service (DWD, Deutscher Wetterdienst) via the OpenData area of the Climate Data Center (CDC). These data are provided in 1611 files, resulting in > 500 million rows of measurement information (or missing values), a format that is poorly suited for further analysis.

Therefore, the data are converted from "long format" to "wide format". The result is a time series with 10 minute frequency containing one column per weather station. The exact columns in the file are: - MESS_DATUM: the datetime values of the time series, representing the index of the time series - list of weather station ids: one column per weather station, represented by the weather station id

From the five numerical measurement values of the original data, only "air temperature at 2m height in °C" was kept.

In addition to the extracted temperature data, a notebook is provided which can be used to extract the other four types of measurements in the same format.

The following files are provided in this dataset: - german_temperature_data_1990_2021.csv, containing the extracted original data (download and transformation, see this notebook). - german_temperature_data_1996_2021_from_selected_weather_stations.csv, containing a selection of the original data from 55 weather stations that have continuously provided a high amount of measurements from 1996-2021 (and thus no change in distribution over time). For the selection process, see this notebook. - zehn_min_tu_Beschreibung_Stationen.txt, additional information about the weather stations. - DESCRIPTION_obsgermany_climate_10min_tu_historical_en.pdf, the official data set description.

The terms of use are described by https://opendata.dwd.de/climate_environment/CDC/Nutzungsbedingungen_German.pdf and https://gdz.bkg.bund.de.

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 July 2025, the average precipitation amounted to 114 liters per square meter, a drastic increase compared to the previous month. The rainiest state in Germany was Saarland.

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.

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.

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.

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.

Ice days per month per weather station from 1981 to 2010.

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.

Instead of relying on broad regional summaries, our drought model captures localized trends with precision — quantifying deficits, duration, and recovery patterns tailored to your area of concern. Each analysis is refined to reflect real conditions and decision-making needs, ensuring accuracy, consistency, and relevance.

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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.

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`.

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A one-year data set for the year 2015 of near-surface air temperature (T$T$), crowdsourced from ‘Netatmo’ citizen weather stations (CWS) in Berlin, Germany, and surroundings was analysed. The CWS data set, which has been quality-checked and filtered in a previous study, consists of T$T$ measurements from several hundred CWS. It was investigated (1) how CWS are distributed among urban and rural environments, as represented by ‘local climate zones’ (LCZ), (2) how LCZ are characterised in T$T$ along the annual cycle and concerning intra-LCZ T$T$ variability, and (3) if significant T$T$ differences between LCZ (ΔT$\Delta T$) can be detected with CWS data. Further, it was investigated how the results from CWS compare to reference data from standard meteorological measurement stations. It can be shown that all ‘urban’ LCZ are covered by CWS, but only few CWS are located in ‘natural’ LCZ (e.g. forests or urban parks). CWS data along the annual cycle show generally good agreement to reference data, though for some LCZ monthly means between both data sets differ up to 1 K. Intra-LCZ T$T$ variability is particularly large during night-time. Statistically significant ΔT$\Delta T$ can be detected with CWS data between various LCZ pairs, particularly for structurally dissimilar LCZ, and the results are in agreement with existing literature on LCZ or the urban heat island. Furthermore, annual mean ΔT$\Delta T$ in CWS data agree well with reference data, thus showing the potential of CWS data for long-term studies. Several challenges related to crowdsourced CWS data need further investigation, namely missing meta data, the non-standard measurement locations, the imbalanced availability in time and space, and potentials to combine CWS and reference data to benefit from the main advantages of both, i.e., the large number of stations and the high quality of data, respectively.

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.

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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.

Instead of relying on a single source or model, our Climate Data product blends decades of historical records with forward-looking projections to deliver a unified view of past, present, and future conditions. Each dataset is refined, harmonized, and tailored to match your specific region, variables, and time horizons—ensuring accuracy, continuity, and context.

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UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands is a vertical profile time series dataset published by the Research Institute for Nature and Forest (INBO). It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (RMI), German Meteorological Service (DWD) and Royal Netherlands Meteorological Institute (KMNI) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (radar) volume, grouped into altitude bins (height) and measured over time (datetime). The data are also available in the Aloft bucket.

Files

VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the VPTS CSV format and are described in vpts-csv-table-schema.json. An overview of what data are available is provided in coverage.csv. Radar metadata can be found at https://aloftdata.eu/radars/.

coverage.csv: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.

vpts-csv-table-schema.json: technical description of the fields in the VPTS data.

be.tgz: VPTS data from 3 radars in Belgium.

de.gz: VPTS data from 18 radars in Germany.

nl.gz: VPTS data from 3 radars in the Netherlands.

Acknowledgements

This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the GloBAM project, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.