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 June 2025, the average precipitation amounted to 61 liters per square meter, an increase compared to the previous month. The rainiest state in Germany was Saarland.

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.

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.

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.

🇩🇪 독일 English This data set includes historical weather data for the station of the DWD (Station number: 02712) at Silvanerweg 6 in Constance over a longer period of time. On July 25, 2017, an amendment to the German Weather Service Act ("DWD Act") came into force. The DWD is legally mandated to provide its weather and climate information largely free of charge. Currently, many geodata such as model predictions, radar data, current measurement and observation data as well as a large number of climate data are available on the Open Data Server https://opendata.dwd.de . The climate data is provided under https://opendata.dwd.de/climate_environment/CDC. The freely accessible data may continue to be used without restrictions in accordance with the "Ordinance on the Determination of the Terms of Use for the Provision of Federal Geodata (GeoNutzV)" with the addition of a source note (https://gdz.bkg.bund.de). With regard to the design of the source notes, the German Meteorological Service (DWD) (pursuant to § 7 DWD Act, § 3 GeoNutzV) requests the following information: The obligation to include provided source notes applies to the unchanged use of spatial data and other services of the DWD. References must also be included when extracts are created or the data format is changed. An illustration of the DWD logo is sufficient as a source reference within the meaning of the GeoNutzV. For further changes, edits, new designs or other modifications, the DWD expects at least one mention of the DWD in central source directories or in the imprint. Indications of change according to GeoNutzV can be, for example: "Data base: German Weather Service, raster data graphically reproduced", "Data base: German weather service, individual values averaged" or "Data base: German weather service, own elements added". In the case of a use that does not meet the intended purpose of the performance of the DWD, enclosed source notes must be deleted. This applies in particular to weather warnings if it is not ensured that they are made available to all users at all times completely and immediately. Source: German Weather Service (DWD)

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.

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:

Regular monitoring

Context

This dataset contains weather details of five most important countries including Germany and Italy which was affected greatly with Covid_19 spread.

Content

It is believed that climate conditions might be one of the major reasons for the spread of covid_19. This Dataset contains climate changes occured from 19th February to 17th April 2020. This contains the climate changes recorded for every 10 mins on the aforementioned countries.

File Description

The file contains below columns:

Temperature - Actual Temperature Recorded in degree celsius Wind_speed - Wind Speed Description - Description of the current weather Weather - Categorical value depicts the types of weather name - Depicts the country name temp_min - Minimum temperature recorded temp_max - Maximum temperature recorded

Other variables are pretty much self explanatory.

Acknowledgements

As part of my thesis project, this dataset was being prepared with a help of web scraper which will trigger an open source REST API end point for every 10 minutes. It was hosted in an EC2 instance which will update a CSV file periodically. Thought that this could contribute for the analysis of Covid_19 spread, hence shared the same.

Hope this could be useful!

Inspiration

As mentioned earlier, Climate could be one of the significant factors which spreads covid_19. Need to analyse further on the same. Italy could be considered for the research as we have the climate data for that country. Alongside, this country was affected largely.

🇩🇪 독일

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.

WMS service for the climatological data of the Climate Data Center (CDC)

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

As part of the FAIR project, open meteorological data from the German Weather Service were prepared for user-friendly use. In the near future, these will be made available via the FAIR portal. With regard to historical data, you will find the data products via the FAIR portal — Cosmo-REA6 (The Reanalysis of the German Weather Service) — Cosmo-REA6 optimised wind (as part of the project of refined data set for the wind industry) Historical weather measurements on ships — Prognostic data from the forecast model MOSMIX A processing fee will be charged for all data after the end of the project.

As part of the FAIR project, open meteorological data from the German Meteorological Service were processed for user-friendly use. In the near future, these will be made available via the FAIR portal. With regard to historical data, you will find the data products via the FAIR portal - COSMO-REA6 (The reanalysis of the German Weather Service) - COSMO-REA6 optimized wind (data set refined within the project for the wind industry) - Historical weather measurements on ships - Predictive data from the prediction model MOSMIX A processing fee will be charged for all data after the end of the project.

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.

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`.

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,
12h-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".

Web Map Service of the German Weather Service for climatological data