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 ****. 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.

In 2023, the average summer temperature in South Korea was around **** degrees Celsius, up from **** degrees Celsius in the previous year. The highest temperature since 2000 was **** degrees Celsius in 2018, while the lowest temperature was **** degrees Celsius in 2003.

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.

[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 difference between the 'lower' values before and after this update is 0.09°C.]What does the data show? This dataset shows the change in summer average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare summer average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Summer Average Temperature Change is 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 Summer Average Temperature Change, an average is taken across the 21 year period.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?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tas summer change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas summer change 2.0 median' is named 'tas_summer_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas summer change 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, the Summer Average Temperature Change was 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 ‘higher’ 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 higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period 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 linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

[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 difference between the 'lower' values before and after this update is 0.26°C.]What does the data show? This dataset shows the change in summer maximum air temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. The dataset uses projections of daily maximum air temperature from UKCP18. For each year, the highest daily maximum temperature from the summer period is found. These are then averaged to give values for the 1981-2000 baseline, recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare summer maximum temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Summer Maximum Temperature Change is 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 Summer Maximum Temperature Change an average is taken across the 21 year period.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?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tasmax summer change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tasmax summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tasmax summer change 2.0 median' is named 'tasmax_summer_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tasmax summer change 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, the Summer Maximum Temperature Change was 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 ‘higher’ 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 higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period 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 linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Mean July temperatures for land areas of the world in degrees Celsius. Fahrenheit and Celsius values are included in pop-ups. Data derived from WorldClim Global Climate Data daily July average temperatures from 1950-2000.

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 tas_summer_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.

It is expected that the highest temperature in Summer on average will be approximately *** degrees Fahrenheit hotter in New York City by 2050 compared to the year 2000. The Winter lowest temperature will be *** degrees hotter by 2050. The city of Chicago, Illinois expects an even higher increase of *** degrees Fahrenheit in Summer's highest temperature and an increase of *** 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 ** 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 ***** cooling degree days in the U.S., compared to ***** in 2009. More cooling degree days indicate an increase in temperature, leading to a greater likeliness of using air conditioning.

[update 28/03/24 - This description previously stated that the the field “2001-2020 (recent past) change” was a percentage change. This field is actually the difference, in units of mm/day. The table below has been updated to reflect this.][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 but for the fixed periods which are expressed in mm, the average difference between the 'lower' values before and after this update is 0.04mm. For the fixed periods and global warming levels which are expressed as percentage changes, the average difference between the 'lower' values before and after this update is 4.65%.]What does the data show?

This dataset shows the change in summer precipitation rate for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.

The dataset uses projections of daily precipitation from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a percentage change (%) relative to the 1981-2000 value. This enables users to compare summer precipitation trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.

      Period
      Description


      1981-2000 baseline
      Average value for the period (mm/day)


      2001-2020 (recent past)
      Average value for the period (mm/day)


      2001-2020 (recent past) change
      Change (mm/day) relative to 1981-2000


      1.5°C global warming level change
      Percentage change (%) relative to 1981-2000


      2°C global warming level change
      Percentage change (%) relative to 1981-2000


      2.5°C global warming level change
      Percentage change (%) relative to 1981-2000


      3°C global warming level change
      Percentage change (%) relative to 1981-2000


      4°C global warming level change
      Percentage change (%) relative to 1981-2000

What is a global warming level?

The Summer Precipitation Change is 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 Summer Precipitation Change, an average is taken across the 21 year period.

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?

These data contain a field for each warming level and the 1981-2000 baseline. They are named 'pr summer change', the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'pr summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'pr summer change 2.0 median' is named 'pr_summer_change_20_median'.

To understand how to explore the data, refer to the New Users ESRI Storymap.

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr summer change 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, the Summer Precipitation Change was 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 ‘higher’ 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 higher fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period 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 links

 For further information on the UK Climate Projections (UKCP).
 Further information on understanding climate data within the Met Office Climate Data Portal.

The annual mean temperature in the United Kingdom has fluctuated greatly since 1990. Temperatures during this period were at their highest in 2022, surpassing ** degrees Celsius. In 2010, the mean annual temperature stood at **** degrees, the lowest recorded during this time. Daily temperatures Average daily temperatures have remained stable since the turn of the century, rarely dropping below ** degrees Celsius. In 2010, they dropped to a low of **** degrees Celsius. The peak average daily temperature was recorded in 2022 when it reached **** degrees. This was an increase of *** degree Celsius compared to the long-term mean, and the most positive deviation during the period of consideration. Highs and lows The maximum average temperature recorded across the UK since 2015 was in July 2018. This month saw a maximum temperature of **** degrees Celsius. In comparison, the lowest monthly minimum temperature was in February of the same year, at just minus *** degrees. This was an especially cold February, as the previous year the minimum temperature for this month was *** degrees.

Temperature in Japan increased to 13.11 celsius in 2024 from 13 celsius in 2023. This dataset includes a chart with historical data for Japan Average Temperature.

[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 difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Extreme Summer Days is the number of days per year where the maximum daily temperature is above 35°C. It measures how many times the threshold is exceeded (not by how much) in a year. Note, the term ‘extreme summer days’ is used to refer to the threshold and temperatures above 35°C outside the summer months also contribute to the annual count. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Extreme Summer 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 extreme summer days to previous values.What are the possible societal impacts?The Annual Count of Extreme Summer Days indicates increased health risks, transport disruption and damage to infrastructure from high temperatures. It is based on exceeding a maximum daily temperature of 35°C. Impacts include:Increased heat related illnesses, hospital admissions or death affecting not just the vulnerable. Transport disruption due to overheating of road and railway infrastructure.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Tropical Nights (where the minimum temperature does not fall below 20°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Extreme Summer Days is 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 Count of Extreme Summer Days, an average is taken across the 21 year period. Therefore, the Annual Count of Extreme Summer Days show the number of extreme summer 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 global warming level and two baselines. They are named ‘ESD’ (where ESD means Extreme Summer Days, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Extreme Summer Days 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Extreme Summer Days 2.5 median’ is ‘ExtremeSummerDays_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 ‘ESD 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, the Annual Count of Extreme Summer Days was 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.

This dataset contains 2001-2020 burned areas and climate variables for three regions with Mediterranean climates: South America from 31-46 degrees South, including Chile and the forested Andean region of Argentina; the western United States from 33-49 degrees North from the coast extending to the eastern extent of forest, and the Iberian Peninsula, including all of Spain and Portugal.

Burned areas are polygon shapefiles for all regions except Chile, for which the burn area is represented in a point shapefile. The data sources for the fire shapefiles are: Chile: unpublished, originally from Corporación Nacional Forestal (CONAF) and compiled by Miranda Argentina: unpublished, compiled by Diego Mohr-Bell and others at Centro de Investigación y Extensión Forestal Andino Patagónico (CIEFAP) North America: NIFC 2023 Iberian Peninsula: EFFIS 2022

All of the fire shapefiles are contained within the zip folder fire_areas, and the individual regions are ch_fire (Chile), ar_fire (Argentina), na_fire (North America), ib_fire (Iberian Peninsula). The attributes of the shapefiles are the year and the fire area in square kilometers. For Chile, the fire start dates were documented. If the fire started in June-December, the year assigned is advanced by 1 from the original year. This is because the summer fire season straddles the calendar year boundary, and the fire year is assigned based on the year with most of the summer season. For Argentina, the end dates of the fire were available, so these end dates were used to assign the fire year.

Annual summaries of fire area and climate variables are provided in the fire_ann_all.csv file. The columns in this file are: year wetdryzone: dry if mean annual aridity index <1; wet if mean annual aridity index >1 cont: location, either Iberian Peninsula, North America, or South America area_km2: total burned area in square km AIann: annual aridity index calculated as total precipitation over total potential evapotranspiration AIjas: summer aridity index for July-September in northern hemisphere; January-March, southern hemisphere vpd: mean annual vapor pressure deficit (kPa) vpd_jas: mean summer vapor pressure deficit (kPa) def: mean annual climatic water deficit (mm) def_jas: summer climatic water deficit (mm)

Climate data were obtained from TerraClimate (Abatzoglou et al. 2018).

Mean annual summaries of fire and climate data by aridity index zone are provided in the file AI_bins_meanannual.csv. AI zones/bins are in increments of 0.2. Columns are: cont: location, either Iberian Peninsula, North America, or South America AI_max: maximum AI for the AI zone. For example, if the value is 0.2, the zone is from AI=0 to AI=0.2 zone_area: total area of the climate zone in square kilometers forest_area: total area forested in 2000 in square kilometers (Potapov et al. 2022) fire_area: total area burned from 2001-2020 in square kilometers (same sources as fire shapefiles) frac_forest: fraction of climate zone area that is forested frac_fire: fraction of the climate zone area that was burned mean_patch_area: mean size of forest patches identified within each AI zone in square kilometers fwi: mean fire weather index from the European Center for Medium-range Weather Forecasts (Vitolo et al. 2020)

Original data sources: Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. (2018), Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 170191.

European Forest Fire Information System EFFIS (2022). Burnt area mapped using Sentinel-2/MODIS images. Accessed September, 2022.

NIFC 2023. Interagency Fire Perimeter History https://data-nifc.opendata.arcgis.com/search?tags=Category%2Chistoric_wildlandfire_opendata, downloaded 3/25/23.

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing, 3.

Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.

Some of the CYR rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This file includes a downscaled projection of decadal average summer (June, July, August) temperature (in #176;C) for the decades 2010-2019, 2020-2029, and 2060-2069 at 771x771 meter spatial resolution. The file represents a decadal mean calculated from seasonal averages, which in turn were calculated from monthly means, using the A2 emissions scenario. Historic data is based on measured climate information rather than modeled projections.

Temperature in India increased to 25.43 celsius in 2024 from 25.03 celsius in 2023. This dataset includes a chart with historical data for India Average Temperature.

During 2023, the average temperature recorded in India was ***** degrees Celsius, a slight increase from the ** degrees Celsius recorded in the previous year. This represented the highest average temperature recorded in the South Asian country since 2017.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Digital grid dataset of minimum monthly temperature (July) for the period of 1950-2000 of Hindu Kush Himalayan (HKH) Region. The dataset is derived from WorldClim (http://www.worldclim.org/), and major climate databases compiled by the Global Historical Climatology Network (GHCN),the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet. Monthly Precipitation data set consists of 12 raster files, one for each month, showing minimum mean values derived from monthly temperature readings. The data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid.