The average annual temperature in the Netherlands in 2022 stood at 11.6 degrees Celsius. This represented a rise of 1.1 degrees Celsius in comparison to the previous year. The European country reported its highest average temperature at 11.7 degrees Celsius in 2020.

This statistic displays the average maximum monthly temperature in the Netherlands over the past 20 years. It shows that over the past twenty years the month with the highest average maximum temperature has been August, with an average temperature of 21.6 degrees Celsius. On average, January has been the coldest month.

Temperature in Netherlands remained unchanged at 11.68 celsius in 2023 from 11.68 celsius in 2022. This dataset includes a chart with historical data for Netherlands Temperature.

This table presents climate data from the Dutch weather station De Bilt (source: KNMI). The average winter and summer temperatures, which started in 1800, are the longest current series shown in the table. The series on the average year temperature and on hours of sunshine per year started in 1900. For the number of days below of above a certain temperature (ice days, summery days) the ranges started between 1940 and 1950. The complete set of climate data is available from 1980 onwards.

Data available from: 1800-2014.

Status of the figures: All data are definite.

Changes as of 19 April 2016: Not. This table has been discontinued.

When will new figures be published? Not applicable anymore.

Data on the weather and climate in The Netherlands can be found on the website of the Royal Netherlands Meteorological Institute KNMI

Gridded files of daily mean temperature in the Netherlands. Based on 33 -35 automatic weather stations of the KNMI.

Transformed daily time series for average temperature for the KNMI'14 scenarios for around 2030, 2050 and 2085 for 14 stations in the Netherlands (revised 2015). Can also be downloaded through http://www.klimaatscenarios.nl/toekomstig_weer/transformatie/index.html

Average annual temperatures in Amsterdam, Netherlands are projected to rise under the different Representative Concentration Pathways (RCP), based on the historic baseline of 9.8 degrees Celsius (°C). Under the RCP 4.5 intermediate emission scenario, it is expected that temperatures will rise to 10.7 °C in the next decades and to 11.1 °C by mid-century. Temperatures will continue to rise to reach 11.5 °C by 2099, following the same scenario.

Gridded files of average yearly minimum temperature in the Netherlands over the period 1981-2010 (normal period). Based on 28 automatic weather stations.

Gridded files of average monthly temperature in the Netherlands over the period 1981-2010 (normal period). Based on 28 automatic weather stations.

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Netherlands Cooling Degree Days data was reported at 227.080 Degrees Celsius in 2020. This records a decrease from the previous number of 231.020 Degrees Celsius for 2019. Netherlands Cooling Degree Days data is updated yearly, averaging 120.810 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 309.380 Degrees Celsius in 2018 and a record low of 23.370 Degrees Celsius in 1974. Netherlands Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Environmental: Climate Risk. A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

These datasets are associated with the manuscript "Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas." The datasets include (1) ZIP code-level daily average temperature for 2000-2017, (2) ZIP code-level daily counts of Medicare hospitalizations for cardiovascular disease for 2000-2017, and (3) ZIP code-level population-weighted urban heat island intensity (UHII). There are 9,917 ZIP codes included in the datasets, which are located in the urban cores of 120 metropolitan statistical areas across the contiguous United States. (1) The ZIP code-level daily temperature data is publicly available at: https://doi.org/10.15139/S3/ZL4UF9. A data dictionary is also available at this link. (2) The ZIP code-level daily counts of Medicare hospitalizations cannot be uploaded to ScienceHub because of privacy requirements in the data use agreement with Medicare. (3) The ZIP code-level UHII data is attached, along with a data dictionary describing the dataset. Portions of this dataset are inaccessible because: The ZIP code-level daily counts of Medicare cardiovascular disease hospitalizations cannot be uploaded to ScienceHub due to privacy requirements in data use agreements with Medicare. They can be accessed through the following means: The Medicare data can only be accessed internally at EPA with the correct permissions. Format: The Medicare data includes counts of the number of cardiovascular disease hospitalizations in each ZIP code on each day between 2000-2017. This dataset is associated with the following publication: Cleland, S., W. Steinhardt, L. Neas, J. West, and A. Rappold. Urban Heat Island Impacts on Heat-Related Cardiovascular Morbidity: A Time Series Analysis of Older Adults in US Metropolitan Areas. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 178(108005): 1, (2023).

Heating Degree Days data was reported at 4,676.170 Degrees Celsius in 2020. This records a decrease from the previous number of 4,919.320 Degrees Celsius for 2019. Heating Degree Days data is updated yearly, averaging 5,484.010 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 6,609.630 Degrees Celsius in 1996 and a record low of 4,568.620 Degrees Celsius in 2014. Heating Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Environmental: Climate Risk. A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Netherlands NL: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 0.005 % in 2009. Netherlands NL: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 0.005 % from Dec 2009 (Median) to 2009, with 1 observations. Netherlands NL: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Netherlands – Table NL.World Bank.WDI: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;

Gridded files of average yearly maximum temperature in the Netherlands over the period 1981-2010 (normal period). Based on 28 automatic weather stations.

The E-OBS dataset (https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php) consists of gridded fields created from station series throughout Europe. The dataset contains preliminary daily updates of the E-OBS dataset for daily mean temperature. Only the last 60 days are saved in this dataset, so the latest month is completely available at all times after the monthly update. This dataset is currently unavailable on our platform. We are actively working to resolve this issue, but we do not have a definitive timeline for when the download functionality for this dataset will be restored. In the meantime, you can access the dataset directly from the original source using the following alternative link: https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php.

This resource contains observations gathered from the Dutch North Sea Network, and ships in the North Sea taking measurements. Contents include mean temperature charts (averaged every 3 hours), mean sea surface charts, mean wind speed charts, mean wave height charts.

Climate indices were interpolated from observations and transformed time series at automatic weather stations and precipitation stations in the Netherlands for the period 1991-2020 and for the KNMI'23 scenarios around 2050 and 2100. The interpolated data is projected onto a 1 km by 1 km grid, without corrections for local land cover features such as cities or forests. However, large-scale climatic patterns, such as distance from the sea and elevation, are accounted for in the interpolation. The climate indices include annual and seasonal precipitation, the number of days per year with at least 15 mm or 25 mm of precipitation, and the maximum precipitation deficit, including median values and estimates for a 10-year recurrence of precipitation deficit. Temperature-related climate indices include average minimum and maximum temperatures by season and year, the number of ice days, frost days, warm days, summer days, tropical days, and tropical nights, as well as Cooling Degree Days and Heating Degree Days. Data was compiled by interpolating observations from stations that had a nearly complete set of measurements for the period 1991-2020.

Data used in Hallmann CA, Zeegers T, van Klink R, Vermeulen R, van Wielink P, Spijkers H, van Deijk J, van Steenis W, Jongejans E (2020) Declining abundance of beetles, moths and caddisflies in the Netherlands. Insect Conserv Divers 13:127-139

Data were collected at two groups of sites: De Kaaistoep and Wijster, both in the Netherlands. In addition, we obtained data from two KNMI weather stations (for De Kaaistoep data: weather station Gilze-Rijen, for Wijster data: weather station Eelde, at, respectively, 3.6 and 40 km from trapping locations), from which we extracted relevant parameters.

Collecting at light in De Kaaistoep De Kaaistoep is a 330 ha managed natural area consisting of heathland, pine forest and grassland. It was established in 1994 on former arable land. Information about the location and management history can be found in the study by Felix and van Wielink (2008). Insects were attracted by light in combination with a white cloth (Supporting Information Fig. S1) over a period of 3.3 h per trapping night, normally starting around sunset (Hallmann et al. 2020). During this sampling period, individuals of the various insect taxa were counted, or were estimated in the case of large numbers. All macro-moths were always counted and identified, while for other groups of insects, between 25 and 100% were collected for identification. Further details of the sampling protocol are given in the study by van Wielink and Spijkers (2013). The data archived her have been collected during 628 trapping nights between 1997 and 2017, on average 30 evenings per year (10–77). Data were available for the period of 1997–2017 for macro-moths (Lepidoptera), beetles (Coleoptera) and ground beetles (Carabidae), while for caddisflies (Trichoptera), lacewings (Neuroptera), true bugs (Hemiptera-Heteroptera and Hemiptera-Auchenorrhyncha) and mayflies (Ephemeroptera) data were available only for the years 2006 and 2009–2017. Of the large number of Coleoptera, only ground beetles, ladybirds and carrion beetles were identified to species up to 2017, accounting for 48 000 of 239 000 beetle specimens. As it is known that the environmental conditions (like temperature) during each trapping night influenced the number of insects caught, we aimed to include relevant covariates in our analyses. Information about the timing and duration of sampling were available for 91.2% of the nights (n = 574), and lacking more in the first few years of sampling than later on. The number of sampling hours per night varied little among years but did increase from an average of 3.1 h (1997–2009) to an average of 3.8 h per night after 2010 (Hallmann et al. 2020). Timing of onset of sampling was roughly at sunset throughout the years, with the exception of the first few years in which sampling started on average up to half an hour after sunset (Hallmann et al. 2020).

Pitfall traps near Wijster A long-term monitoring program using pitfall traps was started at the Wijster Biological Station (and continued by the Foundation Willem Beijerink Biological Station) in two nature reserves in the province of Drenthe: National Park Dwingelderveld and the fragmented, but increasingly reconnected Hullenzand. In these reserves restoration measures, mainly in the form of topsoil removal and reconnection, were carried out during the early 1990s. The pitfall data have been collected between 1959 and 2016 at in total 48 unique locations (mean = 9, range 4–19 operating locations per year). The locations consisted mainly of heathlands, with some forest sites, a forest edge and an abandoned crop field. At each location, three square pitfall traps (25 by 25 cm) were installed: one lethal funnel trap with a 3% formaldehyde solution and two live traps. The traps at each location were spaced 10 m apart. Caught ground beetles (Coleoptera: Carabidae) have been identified at weekly intervals. Further details on the sampling protocol and the area are given in the study by den Boer and van Dijk (1994). Because we are only interested in recent trends in insect abundances, and because sampling protocols were not consistent in the early years, we here only archive data collected since 1986. We document two types of data: i) the annual sums per species and location for the period of 1986–2016, and ii) the weekly sums per species and location that have been fully digitised and checked: 2002–2017. Annual totals 1986–2016. In total, 7778 records of species-specific counts are archived, which amounted to 264 986 individual ground beetles. For 20 records, we used multiple imputation (Onkelinx et al. 2017) to derive more reliable estimates for suspected erroneous counts. This method is based on the correlation structure between years and between other species. Note that in the years 1998–2001, no monitoring took place, and 2004 was omitted because of incomplete catches.

Species weights For biomass estimation we used known species length measurements and known relationships of length to weight (Sabo et al. 2002; García-Barros 2015). For the Carabidae in the Wijster data set, we used the minimum and maximum body length as stated in the Dutch ground beetles field guide (Boeken et al. 2002).

KaaistoepOrder.csv
- Trich = number of Trichoptera individuals - Hemip = number of Hemiptera individuals - Neur = number of Neuroptera individuals - Ephem = number of Ephemeroptera individuals - Hemi.hetero = number of Hemiptera-Heteroptera individuals - Hemi.cica = number of Hemiptera- Auchenorrhyncha individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - start = date and start time of the measurement - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement

KaaistoepColeo.csv - Coleo = number of Coleoptera individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - sunset = date and sunset time - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement

KaaistoepLepi.csv - freq = number of Lepidoptera individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - sunset = date and sunset time - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement

KaaistoepCara.csv - Cara = number of Carabidae individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - sunset = date and sunset time - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement - species = number of individuals of each of 94 Carabidae species

KaaistoepSilphi.csv - Silph = number of Silphidae individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - sunset = date and sunset time - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement - species = number of individuals of each of 6 Silphidae species

KaaistoepCocci.csv - Cocci = number of Coccinellidae individuals - T = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - U = hourly relative humidity (in %; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - FH = hourly average windspeed (in 0.1 m/s) - sunset = date and sunset time - suns.deviate = number of minutes the measurements started after sunset - tdiff = number of hours of measurement - species = number of individuals of each of 23 Coccinellidae species

KaaistoepMacro2.csv (non-zeros) - freq = number of macro-Lepidoptera individuals - name = macro-Lepidoptera species - year = year of measurement - daynr = day of the year

KaaistoepLepiWeights - name = macro-Lepidoptera species - mgSabo = mass (mg) estimated using Sabo et al. (2002) - mgGarciaBarros = mass (mg) estimated using García-Barros (2015)

WijsterWeek.csv - freq = number of ground beetle individuals - date = date of emptying trap - dt = number of days a trap has been open - trap = randomized trap ID - FG = hourly average windspeed (in 0.1 m/s) - TG = hourly average temperature (in 0.1 degrees Celsius; at 1.5 meter height) - RH = hourly sum of precipitation (in 0.1 mm) (negative values for <0.05mm) - UG = hourly relative humidity (in %; at 1.5 meter height) - species = number of individuals of each of 134 ground beetle species

WijsterColeoWeights - name = ground beetle species - mgSabo = mass (mg) estimated using Sabo et al. (2002)

The wind chill heat map shows the wind chill temperature at any location in the Netherlands during an extremely hot summer afternoon. Weather measurements on 1 July 2015 were used for this purpose. This is a hot day that occurs approximately once every 5.5 years in the current climate. The map shows the situation in 2050 with strong climate change (WH scenario). The spatial information (buildings, vegetation, water, trees) is the same for both maps, for which nationally available data have been used. The map shows where it feels relatively warmer and which places in the city are the least comfortable. This concerns the wind chill outside, not inside buildings. The map presents the average wind chill in ºC for the period 12:00-18:00 local time on a hot summer day. We also call this temperature the physiologically equivalent temperature or PET. More information about this heat map wind chill can be found via the Climate Effect Atlas.