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.

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 statistic shows the average temperature in Germany in winter 2023/24, with a comparison to the previous year, by federal state. That winter, the average temperature in 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.

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

The diurnal cycle of both air temperature and wind speed is reflected by considerable differences if open site conditions are compared to forests. This new two-hourly, open dataset covering a high spatial and temporal variability, enables multiple purposes and capabilities due to its diversity and sample size. The dataset provides station pairs, each consisting of one station in the open field and one related station in the forest, located in central Europe, more precisely in southern Germany in the Black Forest (Kinzig; Breg; Brugga) and the Bavarian Alps (Dreisäularbach; Nationalpark Berchtesgaden) as well as the Austrian Alps (Brixenbachtal). Associated meta data specify parameters to characterize the environment and the reference between the paired stations.

The air temperature measurements consist of 128 station pairs from 6 winter seasons and 6 different study sites with a total amount of 173 682 (time steps with availability of open and forest values). The wind speed measurements consist of 64 station pairs from 3 winter seasons and 4 different study sites with a total amount of 115 211. The dataset was initially collected to study the spatio-temporal variability of micrometeorological variables describing the energy balance of the snowpack, but is provided for multiple purposes as examining forest effects on micrometeorological data, validating climate or snow models as well as developing new transfer functions.

Boundary conditions are given below and a comprehensive description of the dataset including analyses and applications follows in the open access article:

Klein, M.; Garvelmann, J.; Förster, K. Revisiting Forest Effects on Winter Air Temperature and Wind Speed—New Open Data and Transfer Functions. Atmosphere 2021, 12, 710. https://doi.org/10.3390/atmos12060710

Meta data The meta data consists of 12 descriptive characteristics. Pair_ID gives an identification name which includes the year of sampling and the acronym of the study site as well as both stations. The Location parameter is a local description of the study site. Elevation, Exposure and Slope have values for the open and forest stations, while Effective_LAI, Canopy_Openness and Distance_Forest_Edge stands for the forest station. With Distance_Open_Station the distance between both stations is designated. The Exposure parameter is defined counterclockwise as follows: 0° and 360° is north, 90° is west and consequently 180° is south and 270° east. Only a few parameters of Distance_Forest_Edge and Distance_Open_Station are not available. These values are marked with NA.

Pair_ID: Identification of the station pair [-]

Location: Local description [-]

Elevation_Open: Elevation in the open field [m a.s.l.]

Elevation_Forest: Elevation in the forest [m a.s.l.]

Exposure_Open: Exposure in the open field counterclockwise (0°/360° = north; 90° = west, etc.)

Exposure_Forest: Exposure in the forest counterclockwise (0°/360° = north; 90° = west, etc.)

Slope_Open: Slope in the open field [°]

Slope_Forest: Slope in the forest [°]

Effective_LAI: Effective leaf area per ground area [-]

Canopy_Openness: Openness of the forest canopy [%]

Distance_Forest_Edge: Distance of the forest station to the closed forest edge [m]

Distance_Forest_Station: Distance between the paired stations [m]

Time series data The time series data consists of air temperature datasets and wind speed datasets, which are named after the Pair_ID described above. According to the two-hour intervals, there are 12 measurements per day. The datasets are structured in the same way as follows: The time stamp (Heading: Date), the measurement in the open (Heading: Air_Temp_Open; Wind_Open) and the measurement in the forest (Heading: Air_Temp_Forest; Wind_Forest). Missing values are marked with NA. Remaining information in terms of number of stations, distribution of observations concerning the study sites and winter seasons, the absolute number of available measurements of both stations as well as additional information are listed following.

Air temperature

128 station pairs (73 open; 59 forest)

Kinzig – KIN (9 station pairs/2012; 10 station pairs/2013)

Breg – BRE (7 station pairs/2012; 9 station pairs/2013; 8 station pairs/2014)

Brugga – BRU (5 station pairs/2013; 14 station pairs/2014; 5 station pairs/2015)

Brixenbachtal – BRX (3 station pairs/2015)

Dreisäulerbach – DSB (7 station pairs/2016; 3 station pairs/2017)

Nationalpark Berchtesgaden – NPB (8 station pairs/2015; 26 station pairs/2016; 14 station pairs/2017)

173 682 total measurements with both values available

Variables: Date [yyyy-MM-dd hh:mm:ss]; Air_Temp_Open [°C]; Air_Temp_Forest [°C]

2 h time interval between measurements

Indication for missing value: NA

Additional information: Air temperature values measured at open stations corrected for radiative heating. Near surface wind speed is measured at 2 m above surface.

Wind speed

64 station pairs (27 open; 34 forest)

Brugga – BRU (5 station pairs/2015)

Brixenbachtal – BRX (3 station pairs/2015)

Dreisäulerbach – DSB (7 station pairs/2016; 3 station pairs/2017)

Nationalpark Berchtesgaden – NPB (7 station pairs/2015; 25 station pairs/2016; 14 station pairs/2017)

115 211 total measurements with both values available

Variables: Date [yyyy-MM-dd hh:mm:ss]; Wind_Open [ms-1]; Wind_Forest [ms-1]

2 h time interval between measurements

Indication for missing value: NA

Additional information: Near surface wind speed is measured at 2 m above surface.

Author Contributions JG led and supervised the field work to collect the data and compiled the dataset. Editing and preparation referring to the publication by JG, MK and KF.

Acknowledgements The presented data was collected during the following research projects:

“Field Observations and Modelling of Spatial and Temporal Variability of Processes Controlling Basin Runoff during Rain on Snow Events” funded by the German Research Foundation (DFG) and carried out at the Chair of Hydrology (PI Stefan Pohl), University of Freiburg, Germany;

“Alpine water resources research: Observing and modeling the spatio-temporal variability of snow dynamics and water- and energy fluxes” funded by Helmholtz Water Alliance and carried out at the Institute of Meteorology and Climate Research (IMK-IFU, PI Jakob Garvelmann, research group Harald Kunstmann), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany. Technical infrastructure from TERENO;

“Storylines of Socio-Economic and Climatic drivers for Land use and their hydrological impacts in Alpine Catchments (STELLA)” funded by the Austrian climate and energy fond and carried out at the Institute of Geography (PI Ulrich Strasser), University of Innsbruck, Austria.

Many thanks to Daniel Günther, Franziska Zieger, Michael Warscher and others for assistance in field work and Emil Blattmann and the staff from KIT-Campus Alpin for technical support. At the University of Innsbruck Elisabeth Mair led the field work within the STELLA-project. Furthermore, we would like to thank Nationalpark of Berchtesgaden for supporting the micrometeorological and snow hydrological measurement campaign.

Climate change is expected to influence crop growth through frequent drought and heat extremes, and thus, drought and heat tolerance are of increasing importance as major breeding goals for cereal crops in Central Europe. Plant physiological water status traits are suitable for phenotyping plant drought/heat tolerance. The objective of this study was to determine whether relative leaf water content (RLWC), plant canopy temperature (CT), and carbon isotope discrimination (CID) are suitable for phenotyping the drought/heat resistance of German winter wheat for future climate resilience. Therefore, a comprehensive field evaluation was conducted under drier and warmer conditions in Moldova using a space-for-time approach for twenty winter wheat varieties from Germany and compared to twenty regionally adapted varieties from Eastern Europe. Among the physiological traits RLWC, CT, and CID, the heritability of RLWC showed the lowest values regardless of year or variety origin, and there was no significant correlation between RLWC and grain yield regardless of the year, suggesting that RLWC did not seem to be a useful trait for distinguishing origins or varieties under continental field conditions. Although the heritability of CT demonstrated high values, the results showed surprisingly low and nonsignificant correlations between CT and grain yield; this may have been due to a confounding effect of increased soil temperature in the investigated dark Chernozem soil. In contrast, the heritability of CID in leaves and grain was high, and there were significant correlations between grain yield and CID, suggesting that CID is a reliable indirect physiological trait for phenotyping drought/heat resistance for future climate resilience in German wheat.

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.

In January 2025, Germany experienced an overall average of 61 sunshine hours, which was an increase compared to the previous month, despite it being winter. Sunshine hours are also referred to as sunshine duration. As can be seen on this graph, the amount that Germany receives differs by season, even quite starkly just by month. Sunniest states When looking at federal states in Germany in 2024, the sunniest states in summer were Berlin, Brandenburg and Saxony. Confirming popular opinion, Hamburg was indeed the state with less sunshine hours in recent years, though not the least sunny compared to others further down the list. In winter, based on recent figures, Germany counted 392 sunshine hours. These figures may change more in the coming years due to the effects of climate change on the weather all over the country. National weather service The German National Meteorological Service (Deutscher Wetterdienst or DWD) monitors the weather in Germany. The service is a federal authority providing information for the population and conducting scientific research. It is also responsible for issuing official warnings when weather conditions are predicted to be threatening.