The greatest number of heating degree days (HDDs) in the United Kingdom is usually recorded in January and February of each year. In 2023, there were 10.2 HDDs in January, which was a slight decrease from the previous year. The highest number of HDDs during the period in consideration was recorded in February 2018, at 12.4 days. The following year, this figure dropped to some 8.6 HDDs.

Heating homes

Heating degree days are defined as days in which the average temperature is less than 15.5 degrees Celsius. They are used to help measure the demand for energy required to heat buildings. In the United Kingdom, the most common way to heat properties is by far gas central heating.

Cold winters

In January 2023, the average temperature in the UK dropped to just 2.9 degrees Celsius. This was the lowest average monthly temperature recorded since January 2021. That same month, there were 10.7 air frost days across the country, slightly up from 10.2 air frost days recorded in the same month of the previous year. Air frost days are when the minimum air temperature falls below zero degrees Celsius.

The average number of heating degree days in the United Kingdom (UK) peaked in 2010 at 6.8 days, followed by 6.2 days in 2013. In 2021, the average number of heating degree days amounted to 5.5.

This dataset contains global gridded maps of Heating Degree Days (HDD) and Cooling Degree Days (CDD) for three climate scenarios: a historical scenario corresponding to a global mean temperature rise of 1.0°C above pre-industrial levels (based on observations from 2006 to 2016), and two future climate projections for global mean temperature increases of 1.5°C and 2.0°C, respectively, regardless of when these thresholds are reached. HDD and CDD are widely used indicators to measure how much the mean temperature exceeds a reference temperature each day over a given period. They are widely used indicators to examine global temperature-related climate and quantify heating and cooling demand.

Five different maps of HDD and CDD are available for each scenario as NetCDF V4 files (*.nc). These maps relate to different annual statistical indices calculated using 70 climate simulations over a 10-year period: mean, median, 10th percentile, 90th percentile, and standard deviation. The novelty of this dataset lies in the combination of two factors: the representation of global mean temperature rise scenarios for 1.5°C and 2.0°C globally, regardless of when these occur; and the bias-corrected global climate dataset used to calculate HDD and CDD, which involves a large ensemble size at a high global spatio-temporal resolution.

Methods:

The global gridded statistical maps of HDD and CDD were calculated considering 18°C as the baseline temperature. First, the annual HDD and CDD were calculated for each simulated year of each scenario at all geographic locations (a total of 700 simulated years per scenario). Then, the statistical indices across this variability were obtained. Global gridded maps have a spatial resolution of 0.833° x 0.556° (longitude x latitude) over the land surface.

Climate data used:

These global gridded maps of CDD and HDD were calculated using bias-corrected global climate simulations for mean temperature generated using the HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Each scenario involved an ensemble of 70 individual members with 6-hourly mean temperatures at a horizontal resolution of 0.833 longitude and 0.556 latitude for a 10-year period (700 runs per scenario), aiming to ensure internal climate variability. These simulation experiments were run within the climateprediction.net (CPDN) climate simulation environment, using the Berkeley Open Infrastructure for Network Computing (BOINC) framework to distribute a large number of individual computational tasks. This system utilises the computational power of publicly volunteered computers that are globally distributed. The bias-corrected global climate dataset used to calculate these CDD and HDD maps is available at:

Lizana, J.; Miranda, N.D.; Sparrow, S.; Zachau-Walker, M.; Watson, P.; Wallom, D.C.H.; McCulloch, M. (2023): Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform. NERC EDS Centre for Environmental Data Analysis, 28 June 2023. doi:10.5285/9c41e3aa67024bbdad796290a861e968

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average percentage change between the 'lower' values before and after this update is -1%.]What does the data show? A Heating Degree Day (HDD) is a day in which the average temperature is below 15.5°C. It is the number of degrees above this threshold that counts as a Heating Degree Day. For example if the average temperature for a specific day is 15°C, this would contribute 0.5 Heating Degree Days to the annual sum, alternatively an average temperature of 10.5°C would contribute 5 Heating Degree Days. Given the data shows the annual sum of Heating Degree Days, this value can be above 365 in some parts of the UK.Annual Heating Degree Days is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of HDD to previous values.What are the possible societal impacts?Heating Degree Days indicate the energy demand for heating due to cold days. A higher number of HDD means an increase in power consumption for heating, therefore this index is useful for predicting future changes in energy demand for heating.What is a global warming level?Annual Heating Degree Days are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Heating Degree Days, an average is taken across the 21 year period. Therefore, the Annual Heating Degree Days show the number of heating degree days that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each warming level and two baselines. They are named ‘HDD’ (Heating Degree Days), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'HDD 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'HDD 2.5 median' is 'HDD_25_median'. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘HDD 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, Annual Heating Degree Days were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

UKCP09: Gridded datasets of annual values. Heating Degree Days The day-by-day sum of the mean number of degrees by which the air temperature is less than a value of 15.5 °C. The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/. To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

A heating degree day is a measurement which quantifies the demand for energy to heat a building. The first months of each year during this period typically saw some of the highest numbers of heating degree days. February 2018 recorded the highest value throughout this period, at 12.4 degrees Celsius. July and August tend to have the lowest number of heat degree days.

Indicator of energy demand for heating. One Heating Degree Day is one day in which daily mean temperature is below the threshold by 1°C. For example if the average temperature for a specific day is 15°C, this would contribute 0.5 Heating Degree Days to the annual sum.This data-set contains 3 fields for each fixed period (1981-2000, 2001-2020) and Global Warming Level (1.5°C, 2°C, 2.5°C, 3°C, 3.5°C, 4°C) combination: the median, 2nd lowest and 2nd highest among the 12 ensemble members. The fields are named accordingly; e.g. the 2nd lowest at 2.5°C is HDD_25_lowerTo understand the data, refer to the LACS Scientific Detail.To understand how to explore the data, see the User Guides available on the Climate Data Portal.

[Dataset has been replaced by a newer version - http://datashare.is.ed.ac.uk/handle/10283/2813] This dataset was produced for the study 'What is the uncertainty in degree day projections due to different calibration methodologies?' (Holmes et al, 2016, Journal of Climate, submitted). Heating and Cooling degree day projections are calculated using various different calibration strategies, on a month-by-month basis and on the 25km grid of the HadRM3-PPE model projections. The software used to produce the NetCDF data was IDL. A list of software for viewing and analysing NetCDF files is available at http://www.unidata.ucar.edu/software/netcdf/software.html . The dataset used the raw data from HadRM3-PPE (http://catalogue.ceda.ac.uk/uuid/f9c7d70b0e6d4c31c067e75ab67cb3cc) and EObs version 12 (http://www.ecad.eu/download/ensembles/download.php).

A heating degree day is the measurement to quantify the demand for energy to heat a building. The first quarters of each year during this period typically saw some of the highest numbers of heating degree days. In the first quarter of 2019 the average number of heating degree days was 9.2. Between 2011 and 2019, the first quarter of 2013 recorded the highest heating degree, at 12.1 degrees Celsius.

UKCP09: 5 km gridded data - Annual averages for heating degree days. The data set contains 12 files (one for each month for the 1961-1990 average period). The individual grids are named according to the following convention: variablename_mmm_Average_Actual.txt where mmm is the month name (e.g. Jan). The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/. To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

Norway and Finland were the European countries with the highest number of heating degree days in 2022. On the other side of the spectrum, Cyprus and Malta were the countries with the highest number of cooling degree days. That means that the number of degrees that the average temperature of Malta was below ** degrees Celsius added up throughout the whole year amounted to ***. ** degrees Celsius is set as the base temperature under which it would be necessary to heat indoor spaces, while degrees Celsius is set as the base temperature above which it would be necessary to start heating indoor spaces. The countries with the highest numbers have the highest need for space heating or cooling throughout the year.

UKCP09 Regional values Annual averages - Heating degree days Long-term averages for the 1961-1990 climate baseline are also available for 14 administrative regions and 23 river basins. They have been produced for all the monthly and annual variables, apart from mean wind speed, days of sleet/snow falling, and days of snow lying, for which data start after 1961. Each regional value is an average of the 5 x 5 km grid cell values that fall within it. The datasets are provided as space-delimited text files. The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/. The data files are obtained by clicking on the links in the table below. Each text file contains values of the 1961-1990 baseline average for each administrative region and for each river basin. Monthly variables have 12 values for each region (one for each month) whereas annual variables have just one value (the annual average). To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

NOTE: This dataset replaces version 1 of the same time, which has an error in the fields Hbc,Cbc,Hdelta and Cdelta. This dataset was produced for the study 'What is the uncertainty in degree day projections due to different calibration methodologies?' (Holmes et al, 2017, Journal of Climate). Heating and Cooling degree day projections are calculated using various different calibration strategies, on a month-by-month basis and on the 25km grid of the HadRM3-PPE model projections.

During 2010 to 2022, the largest deviation of the number of heating degree days in the United Kingdom (UK) from the long term mean, was December 2015, in which there was an average of 4.8 less heating degree days when compared to the long term mean of that month. This coincides with the unusually warm winter of 2015/2016.

UKCP09 Regional values Annual averages - Heating degree days Long-term averages for the 1961-1990 climate baseline are also available for 14 administrative regions and 23 river basins. They have been produced for all the monthly and annual variables, apart from mean wind speed, days of sleet/snow falling, and days of snow lying, for which data start after 1961. Each regional value is an average of the 5 x 5 km grid cell values that fall within it. The datasets are provided as space-delimited text files.

The datasets have been created with financial support from the Department for Environment, Food and Rural Affairs (Defra) and they are being promoted by the UK Climate Impacts Programme (UKCIP) as part of the UK Climate Projections (UKCP09). http://ukclimateprojections.defra.gov.uk/content/view/12/689/.

The data files are obtained by clicking on the links in the table below. Each text file contains values of the 1961-1990 baseline average for each administrative region and for each river basin. Monthly variables have 12 values for each region (one for each month) whereas annual variables have just one value (the annual average).

To view this data you will have to register on the Met Office website, here: http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html.

This statistic illustrates the quarterly average change of heating degree days temperature in the United Kingdom (UK) from 2010 to 2013. In the first quarter of 2013 temperature rose by *** degrees.

Data were made available by the National Energy Efficiency Dataset (NEED). Access to the dataset can be found here. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/857035/anon_set_50k_2019.csv Data was complemented with information from Eurostat (Heating degree days and cooling degree days are not available anymore by region in Eurostat. Original dataset from Eurostat can be provided upon request) and the Department of Business, Energy and Industrial Strategy through https://www.gov.uk/government/statistical-data-sets/annual-domestic-energy-price-statistics. The version used in this paper was published on 26-Mar-2020. All the data gathered for this research belongs to a third parties and are available open access in their respective repositories. The raw data is included in the document Dataset.

This global summary of the day and month data set is obtained on a delayed monthly basis from the Climate Prediction Center (CPC) of the National Centers for Environmental Prediction (NCEP). CPC extracts surface synoptic weather observations from the Global Telecommunications System (GTS) and performs limited automated validation of the parameters. The data is then summarized for all reporting stations on a daily basis to current operational requirements related to the assessment of crop and energy production. Data coverage begins in 1979. In 1987 there is a format change and additional parameters were added. Major parameters include maximum temperature, minimum temperature, precipitation, vapor pressure, sea level pressure, maximum relative humidity, and minimum relative humidity. If the maximum or minimum temperatures are not reported, they are estimated from reported air temperatures in the regular synoptic reports when sufficient data exist. Starting in 1994, total sky cover, 3-hourly wind direction and speed, and total snow depth are included. There are approximately 8900 actively reporting stations. Periods of record vary widely among the stations.

CAUTIONARY NOTE: NCEP incorrectly decoded the wind units indicator from February 1, 2001 until 1500 UTC on June 11, 2002, which caused a knots versus meters-per-second problem. Not all stations were affected. Users may, with caution, apply the knots or meters-per-second conversion where it appears to be the correct choice.