In 2023/2024, the average winter temperature in Germany was 4.1 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 17.3 days with a temperature of at least 30 degrees Celcius, which was more than three times the increase compared to 2021.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in 1895. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

A simulation of the projected changes in December to February mean temperatures from the period 1975 to 1995 to the period 2040 to 2060 is shown on this map. According to this projection, the Arctic would experience the greatest warming followed by other areas in northern Canada and central and northern Asia. Temperatures would generally increase as a result of the projected increases in greenhouse gas concentration in the atmosphere. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

In 2023, the average winter temperature in South Korea was around 2.4 degrees Celsius, up more than two degrees Celsius compared to the previous year. The highest temperature since 2000 reached 2.8 degrees Celsius in 2019, while the lowest temperature was -1.4 degrees Celsius in 2012. Thus, the 2023 figure represents the second-highest average winter temperature in this century.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.

Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.

Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Projected changes in winter precipitation in percentages under A1B scenario, multi-model ensemble mean for the time periods 2021-2050 relative to 1961-1990 mean. Map presents changes using ensemble mean of several regional climate models (RCMs), run by different climate modelling communities in the frame of the EU FP6 Integrated Project ENSEMBLES (Contract number 505539). Data are presented as changes in relative terms (according to 1961-1990 period) in spatial resolution of approximately 25 km.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

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.

A simulation of projected changes in the winter (December to February) temperatures from the period 1961 to 1990 to the period 2040 to 2060 for Canada is shown on this map. The temperature changes would not be evenly distributed geographically. The largest warming projected is for the interior and northern parts of the country. Temperatures are projected to continue increasing as the century progresses. Temperatures would generally increase as a consequence of the projected increase in greenhouse gas concentrations in the atmosphere. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

A pre-configured, multi-layer web map for viewing all Average Winter Temperature scenarios. (To launch the map from the Climate Change Open Data site, select "View Metadata" under the "About" heading, then look for the button labeled "Open in Map Viewer" to the upper right.) The map layers depict historical average winter (Dec-Feb) temperature and projected changes in average winter temperature. Geographic units: HUC10. Map layer data include historical (1970-1999) values plus two projections each for two future time periods, 2050s (2040-2069) and 2080s (2070-2099), based on lower and higher greenhouse gas emission scenarios, RCP 4.5 and RCP 8.5. Data classes and symbology by Robert Norheim, Climate Impacts Group, based on the CMIP5 projections used in the IPCC 2013 report. Data source: Mote et al. 2015.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. In March 2015, new Alaska data was included in the nClimDiv dataset. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS. It includes maximum temperature, minimum temperature, average temperature and precipitation. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025.

It is expected that the highest temperature in Summer on average will be approximately 4.8 degrees Fahrenheit hotter in New York City by 2050 compared to the year 2000. The Winter lowest temperature will be 4.2 degrees hotter by 2050. The city of Chicago, Illinois expects an even higher increase of 5.6 degrees Fahrenheit in Summer's highest temperature and an increase of 5.8 degrees in Winter.

Extreme heat in the U.S. – additional information

Projected changes in global average temperature are associated with widespread changes in weather patterns. Scientific studies indicate that extreme weather events, such as heat waves, are likely to become more frequent or more intense within the next few years. These changes may lead to an increase in heat-related deaths in the United States. Outdoor temperatures can affect daily life in many ways. Extreme heat and the combination of high heat and humidity can pose a serious risk for human health. Exposure to extreme heat can lead to heat stroke and dehydration, as well as cardiovascular, respiratory and cerebrovascular disease. When the weather becomes excessively hot, it can be deadly. According to the National Weather Service, heat waves caused 45 fatalities in the United States in 2015.

The average temperatures in the U.S. have risen significantly since 1895. Long-term changes in climate can directly or indirectly affect many aspects of a person’s life. For example, warmer days could increase air conditioning or water supply costs. One way to measure the influence of temperature change on energy demand is by using heating and cooling degree days. Cooling degree days measure the difference between outdoor temperature and a temperature that people generally find comfortable indoors. Cooling degree days have not increased significantly over the past decades. However, a slight increase is evident for this period. In 2014, there were around 1,300 cooling degree days in the U.S., compared to 1,241 in 2009. More cooling degree days indicate an increase in temperature, leading to a greater likeliness of using air conditioning.

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.

The table Global Temperatures by Major City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://redivis.com/datasets/1e0a-f4931vvyg. It contains 239177 rows across 7 variables.

New Zealand Environmental Data Stack (NZEnvDS) comprises a set of 72 spatial layers for environmental modelling and site characterisation. NZEnvDS includes the layers that informed the Land Environments of New Zealand (LENZ), additional layers generated for LENZ that were never publicly released, and additional layers generated since, all covering mainland New Zealand and surrounding inshore islands. The original/top copy of the dataset is available at (https://doi.org/10.7931/m6rm-vz40), but the layers are reproduced here for ease of access. See (https://newzealandecology.org/nzje/3440/) for a publication describing NZEnvDS. Indicates the degree to which winter temperatures deviate from the winter temperature expected given the mean annual temperature. This provides a measure of continentality, with high values in oceanic settings and low values in continental settings (see the following for examples of their use: Leathwick 2001; Leathwick et al. 2005; Leathwick et al. 2006). This layer has the advantage that its correlation with some other commonly used temperature variables (e.g. mean annual temperature) is low (Appendix S2) so can be used together with other temperature variables in a model without confounding their relative effects (Leathwick 1995). Calculated as (from Leathwick 2001): where J is the mean minimum July temperature, T is the mean annual temperature etc, using the LENZ temperature layers for the years 1950–1980 (Leathwick et al. 2002).

Please cite the original paper as attribution when using these layers.