In 2024, the average summer temperature in Germany was 18.5 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 11.5 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 shows the average temperature in Germany in the summers of 2023 and 2024, by federal state. In summer 2024, the average temperature in Berlin was 19.7 degrees Celsius. This made Berlin the warmest federal state in the period in question, followed by Brandenburg and Baden-Württemberg.

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 shows the amount of precipitation in summer in Germany 2024, by federal state. In 2024, the precipitation in summer in Bavaria amounted to 303 liters per square meter, a decrease compared to 315 liters per square meter in summer 2023

This statistic shows the average precipitation amount in summer in Germany from 1960 to 2024. In 2024, summer precipitation in Germany amounted to 240 liters per square meter. This was a decrease compared to the previous year. Figures have fluctuated over the timeline under review.

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`.

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.

Consumer Confidence in Germany decreased to -20.30 points in July from -20 points in June of 2025. This dataset provides the latest reported value for - Germany Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Description

These data were used in the study "Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe" (Fauer et al., 2022, https://doi.org/10.21203/rs.3.rs-2542862/v1). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual and monthly precipitation maxima over different durations are published here. For more detailed information on our work and the modeling of extreme rainfall data, see also Fauer et al. (2021, https://doi.org/10.5194/hess-25-6479-2021).

Files

precipMax_and_covariates.csv: This file contains aggregated rainfall data over different durations and for different stations ("xdat"). Also, it contains covariates for the variables "blocking", "NAO", surface air temperature ("tas"), humidty and their polynomials up the the fourth order.

precip_meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station. The value in "group" corresponds to the value "station" in precipMax_and_covariates.csv.

Abstract of the according study

Extreme precipitation shows non-stationary behavior over time, but also with respect to other large-scale variables. While this effect is often neglected, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. The models are selected with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large scale information. Also, the new model enables insights into the effect of large scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.

Acknowledgements

We would like to thank the German Weather Service (DWD), especially Thomas Junghänel, and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.

Funding

This study is part of the ClimXtreme project (Grant number 01LP1902H) and is sponsored by the Federal Ministry of Education and Research in Germany.

Changelog

added humidity as large-scale covariate

large-scale covariates are normalized to mean=0 and standard deviation=1

Average Summer (Jul-Sep) Temperature (2015-2030) simulated by RegCM3 with ECHAM5 projections as boundary conditions.

Units are degrees Celsius.

These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid.

RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http:users.ictp.itRegCNETmodel.html), and the ICTP RegCM publications web site (http:users.ictp.it~pubregcmRegCM3pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels.

RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http:regclim.coas.oregonstate.edu.

Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http:wwwpcmdi.llnl.govipccmodel_documentationipcc_model_documentation.php.

We provide the storyline data (in NetCDF format) used in the paper: ���The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging��� published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files. Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc: Latent heat flux (ahfl) Sensible heat flux (ahfs) Monthly Global Mean 2m Temperature (GMTT2mMonthly) Maximum 2m Temperature (t2max) Mean 2m Temperature (t2mean) Minimum 2m Temperature (t2max) Soil Wetness (ws) Only for present climate: 850 hPa Temperature (T850) Total Cloud Cover (TCC) 500 hPa Geopotential Height (Z500) Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc: Sea Ice Concentration (SIC) Sea Surface Temperature (SST) Please, note that FESOM uses an unstructured mesh. This work was supported by the Helmholtz-Climate-Initiative through the HI-CAM project (Drivers cluster). Author Goessling acknowledges the financial support by the Federal Ministry of Education and Research of Germany in the framework of SSIP (Grant 01LN1701A). The simulations were performed at the German Climate Computing Center (DKRZ) using the ESM-Tools (Barbi et al. 2021). We thank Sebastian Rast (MPI-M) for support with the spectral nudging in ECHAM, and the ESM-Tools staff for their assistance during the simulation.

This statistic shows the number of summer sunshine hours in Germany in 2023 and 2024, by federal state. In summer 2024, Berlin in the south of Germany had 780 hours of sunshine, making it the sunniest state that year. The state with the least sunshine hours was North Rhine-Westphalia with only 650 sunshine hours in summer 2024.

Average Summer (Jul-Sep) Temperature (2045-2060) simulated by RegCM3 with ECHAM5 projections as boundary conditions. Units are degrees Celsius. These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid. RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http://users.ictp.it/RegCNET/model.html), and the ICTP RegCM publications web site (http://users.ictp.it/~pubregcm/RegCM3/pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels. RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http://regclim.coas.oregonstate.edu/. Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http://wwwpcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php.

description: Difference of Average Summer (Jul-Sep) Temperature (2045-2060 vs 1968-1999) simulated by RegCM3 with ECHAM5 projections as boundary conditions. Units are degrees Celsius. These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid. This dataset depicts differences between the downscaled data for 2015-2030 and 1968-1999. RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http://users.ictp.it/RegCNET/model.html), and the ICTP RegCM publications web site (http://users.ictp.it/~pubregcm/RegCM3/pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels. RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http://regclim.coas.oregonstate.edu/. Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http://wwwpcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php.; abstract: Difference of Average Summer (Jul-Sep) Temperature (2045-2060 vs 1968-1999) simulated by RegCM3 with ECHAM5 projections as boundary conditions. Units are degrees Celsius. These data were generated by the regional climate model RegCM3 with boundary conditions from a GCM future climate projections. The data were downscaled statistically by calculating differences (anomalies) between the RegCM3 results with GCM-driven boundary conditions for 1968-99 and those for a future period, in this case 2015-2030. The anomalies were added (temperatures) or multiplied (precipitation) to a climate baseline from PRISM (Parameter-elevation Regressions on Indepenent Slopes Model - prism.oregonstate.edu) data based on historical observations. The PRISM baseline was calculated as average monthly climate conditions for 1968-1999 reprojected the results to the BLM Albers 4km grid. PRISM data are provided in a 2.5 arc-minute lat-lon grid. This dataset depicts differences between the downscaled data for 2015-2030 and 1968-1999. RegCM3 is the third generation of the Regional Climate Model originally developed at the National Center for Atmospheric Research during the late 1980s and early 1990s. Details on current model components and applications of the model can be found in numerous publications (e.g., Giorgi et al, 2004a,b, Pal et al, 2007), the ICTP RegCNET web site (http://users.ictp.it/RegCNET/model.html), and the ICTP RegCM publications web site (http://users.ictp.it/~pubregcm/RegCM3/pubs.htm). The Western North America domain has a horizontal grid spacing of 15 km and 18 vertical levels. RegCM3 requires time-dependent lateral (wind, temperature, and humidity) and surface [surface pressure and sea surface temperature (SST)] boundary conditions that are updated every 6 hours of simulation. Lateral boundary conditions are derived from General Circulation Model (GCM) output or observations (e.g. NCEP). Additional information can be found at: http://regclim.coas.oregonstate.edu/. Global simulations from the Max Planck Institute (Germany) climate model ECHAM5 were part of a suite of model results used in the 4th Climate Model Inter-comparison Project (CMIP4) and the Intergovernmental Panel for Climate Change 4th Assessment Report. Details and documentation of the model can be found on the CMIP website: http://wwwpcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php.

This dataset of meteorological observations (temperature, humidity, wind) was collected during four field campaigns in Stuttgart, Germany, in summer 2017, winter 2017, summer 2018, and winter 2018. Doppler lidars and a microwave radiometer were deployed to investigate horizontal structures of intra-urban horizontal wind field patterns and vertical structures of the urban boundary layer. The measurements were conducted within the project ‘Three-dimensional Observation of Atmospheric Processes in Cities’ (3DO). 3DO is one of three modules of the programme ‘Urban Climate Under Change’, [UC]2, funded by the German Federal Ministry of Education and Research (BMBF). More information on the goals of the UC]2 programme can be found on http://www.uc2-program.org/ and in the overview papers listed below. Dieter Scherer, Felix Ament, Stefan Emeis, Ute Fehrenbach, Bernd Leitl, Katharina Scherber, Christoph Schneider, and Ulrich Vogt (2019): Three-Dimensional Observation of Atmospheric Processes in Cities. Meteorologische Zeitschrift 2, 2019. doi: 10.1127/metz/2019/0909. Dieter Scherer, Florian Antretter, Steffen Bender, Jörg Cortekar, Stefan Emeis, Ute Fehrenbach, Günter Gross, Guido Halbig, Jens Hasse, Björn Maronga, Siegfried Raasch, und Katharina Scherber (2019): Urban Climate Under Change [UC]2 – A National Research Programme for Developing a Building-Resolving Atmospheric Model for Entire City Regions. Meteorologische Zeitschrift 2, 2019. doi:10.1127/metz/2019/0913.

In April 2025, the average precipitation amounted to 31 liters per square meter, an increase compared to the previous month. The rainiest state in Germany was Saarland.

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.

• Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2024.
• Average, minimum and maximum 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 `FRCHEM_2024_AirTemperature_MetaData.txt`.

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 22.4 degrees Celsius. On average, January has been the coldest month.

Route design and flying insect sampling Sampling was carried out along 211 routes from 1 - 30 June 2018 in Denmark, and along 67 routes between 25 June - 8 July 2018 in Germany. Across both countries, routes were created across five land cover types: farmland, grassland, wetland, forest and urban areas. No route could be designed to fit 100% to one land cover type, but each route was designed to target a specific land cover type. Each sampling event was either driven in one direction for 10 km or 5 km driven back and forth, to cover as much of the targeted land cover type as possible. The routes were constructed in ArcGIS and QGIS using information from Google Earth, Google Maps, OpenStreetMap (OSM), including data from Danish authorities on land cover types in Denmark, and also using the German ATKIS data (Amtliches Topographisch-Kartographisches Informationssystem) in Germany. Based on the target land cover, in Denmark, 64 urban, 59 farmland, 63 grassland, 62 wetland and 66 forest routes were designed. In Germany, 12 urban, 15 farmland, 12 grassland, 17 wetland and 14 forest routes were created. Sampling of each route was carried out once during two time intervals on the same the day: between 12-15 h (midday) and again between 17-20 h (evening) with a maximum speed of 50 km/h and weather conditions of at least 15°C, an average wind speed of maximum 6 m/s and no rain. Insects were collected in individual sampling bags that were placed in 96% pure ethanol and stored in double-sealed plastic bags before the citizen scientists sent the samples back to the research institutions. Flying insect biomass Insects were removed from the sampling bag with a squeeze bottle containing 96% EtOH and forceps. Empty 15 or 50 ml centrifuge tubes were weighed, and the insects were transferred to the tubes. The insects were dried overnight at 50 ̊C in an oven (>18hrs), and the tubes containing the dry insects were weighed (Mettler Toledo ME303 in Denmark, Quintix® Precision Balance 310 g x 1 mg in Germany) to obtain sample biomass (in total mg). Environmental variables We extracted land use predictors for insect biomass from four buffer zones for each route: 50 m, 250 m, 500 m, and 1000 m in five categories; urban, farmland, grassland, wetland, and forest. The buffers were calculated as linear buffers around each route. Land use intensity data for Denmark were extracted for farmland and urban routes. Other variables We extracted potential car stop variables to account for sampling heterogeneity. We obtained the number of traffic lights or stops of any type (e.g. roundabouts, pedestrian crossings, stop signs, railroad crossings) within a 25-30 m buffer using OSM. For Danish routes, we obtained the number of roundabouts using data from the Danish Map Supply provided by SDFE (Agency for Data Supply and Efficiency) (GeoDenmark-data), since data on roundabouts in Denmark was limited to three records in OSM. Mean hourly temperature and wind was extracted for each route including date and time band from the nearest weather station using the rdwd R package for German routes. For Danish routes, temperature (within increments from 15-20, 20-25 and 25-30 ℃), average wind speed (within increments from light Breeze 1.6-3.3, gentle breeze: 3.4-5.5, and moderate breeze 5.5-7.9 metre/second), and sampling time (hh:mm) were registered by the citizen scientists. Aim In this study, we assessed the importance of local to landscape-scale effects of land cover and land use on flying insect biomass. Our main prediction was that insect biomass would be lower within more intensely used land, especially in urban areas and farmland. Location Denmark and parts of Germany. Methods We used rooftop-mounted car nets in a citizen science project (‘InsectMobile’) to allow for large-scale geographic sampling of flying insects. Citizen scientists sampled insects along 278 five km routes in urban, farmland and semi-natural (grassland, wetland and forest) landscapes in the summer of 2018. The bulk insect samples were dried overnight to obtain the sample dry weight/biomass. We extracted proportional land use variables in buffers between 50 and 1000 m along the routes and compiled them into land cover categories to examine the effect of each land cover, and specific land use types, on insect biomass. Results We found a negative association between urban cover and insect biomass at a landscape-scale (1000 m buffer) in both countries. In Denmark, we also found positive effects of all semi-natural land cover types, i.e. protected grassland (largest at the landscape-scale, 1000 m) and forests (largest at intermediate scales, 250 m). Protected grassland cover had a more positive effect on insect biomass than forest cover. The positive association between insect biomass and farmland was not clearly modified by any variable associated with farmland use intensity. The negative association between insec...

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.

• Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.
• Average, minimum and maximum 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 `FRCHEM_2021_AirTemperature_MetaData.txt`.

Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.