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.

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 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 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.

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.

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

Methods: Statistical downscaling of EURO-CORDEX data is performed, merging MOS (Model Output Statistics) downscaling with surface parameterization techniques (Böhner & Antonic 2009; Böhner & Bechtel 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg & Böhner (2023).

Böhner, J. & Antonic, O. (2009): Land-Surface Parameters Specific to Topo-Climatology. – In: Hengl, T & Reuter, H.I. [Eds.]: Geomorphometry: Concepts, Software, Applications. – Developments in Soil Science, Elsevier, Volume 33, 195-226, https://doi.org/10.1016/S0166-2481(08)00008-1.

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.

Böhner, J. & Wehberg, J.-A. (2022): Schlussbericht zum Verbundvorhaben Standortsfaktor Wasserhaushalt im Klimawandel (WHH-KW); Teilvorhaben 4: Klimadaten. Universität Hamburg/Centrum für Erdsystemforschung und Nachhaltigkeit (CEN)/Institut für Geographie/Abt. Physische Geographie. Waldklimafonds, Bundesministerium für Ernährung und Landwirtschaft, Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit. 14 Seiten.

Wehberg, J.-A. & Böhner, J. (2023): Hochaufgelöste Klimaprojektionen für Deutschland. Forstliche Forschungsberichte München 224. Schriftenreihe des Zentrums Wald-Forst-Holz Weihenstephan, ISBN 3-933506-55-7, pp. 69-78.

Quality: --

Units: degC; degC; degC; mm; MJ/m2; hPa; m/s; kg/kg; percent; mm; hPa

ScaleFactors: 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 0.1; 1; 1; 0.1; 1

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 stored into one NetCDF-file per year and variable and uploaded as tar-archives - one per variable, model and run. The file size of the netCDF files differs between 36 and 206 GB per future scenario simulation and variable (95 years) and between 21 and 113 GB per historical run and variable (56 years).

Format: netCDF

DataSources: EURO-CORDEX data published via ESGF (https://cordex.org/data-access/esgf/). Jacob, D., Petersen, J., Eggert, B. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14, 563–578 (2014). https://doi.org/10.1007/s10113-013-0499-2

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

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:

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.

Observations from weather stations in Germany (Deutscher Wetterdienst) 1960-2020

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Regular monitoring

Context

Jena Climate is weather timeseries dataset recorded at the Weather Station of the Max Planck Institute for Biogeochemistry in Jena, Germany.

Content

Jena Climate dataset is made up of 14 different quantities (such air temperature, atmospheric pressure, humidity, wind direction, and so on) were recorded every 10 minutes, over several years. This dataset covers data from January 1st 2009 to December 31st 2016.

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".

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

Data files which are used for figure 5 in the extended abstract ems-2018

Business Confidence in Germany decreased to 88.10 points in November from 88.40 points in October of 2025. This dataset provides the latest reported value for - Germany Business Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The rainfall data inter-comparison dataset is a collection of precipitation statistics calculated from the hourly nationwide German radar climatology (RADKLIM) and radar online adjustment (RADOLAN) composites provided by the German Weather Service (Deutscher Wetterdienst, DWD), which were combined with rainfall statistics derived from rain gauge data for inter-comparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset.

The dataset consists of two point shapefiles which are readable with all common GIS. It constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 - 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, this data collection has the potential to benefit all users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

Spatial extent: Germany

Spatial Resolution: 1 x 1 km

Time period: 2006 - 2017

Data Format: Two point shapefiles

Compared precipitation datasets: RADKLIM, RADOLAN, rain gauge data

Selection of calculated precipitation statistics:

Annual precipitation sum

Mean annual precipitation sum

Mean seasonal precipitation sums

Number of days exceeding a daily precipitation of 1 mm

Number of days exceeding a daily precipitation of 20 mm

Mean daily precipitation sum of all days exceeding a precipitation sum of 1 mm

Mean daily precipitation sum of all days exceeding a precipitation sum of 20 mm

Number of NoData entries

An Excel file with detailed information on all parameters and on original data sources is also included in the dataset.

Original data source URLs:

RADKLIM (DWD): https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/reproc/2017_002/bin/

RADOLAN (DWD): ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/historical/bin/

Rain Gauge Data (DWD): https://opendata.dwd.de/climate_environment/CDC/observations_ germany/climate/1_minute/precipitation/

Location of wind energy plants (UFZ): https://www.ufz.de/record/dmp/archive/5467/de/

SRTM DEM (DLR): https://geoservice.dlr.de/egp/