The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in April 2025, the average temperature across the North American country stood at 12.02 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

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.

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

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

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

Q: What was the average temperature for the month? A: Colors show the average monthly temperature across the contiguous United States. White and very light areas had average temperatures near 50°F. Blue areas on the map were cooler than 50°F; the darker the blue, the cooler the average temperature. Orange to red areas were warmer than 50°F; the darker the shade, the warmer the monthly average temperature. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments collect the highest and lowest temperature of the day at each station over the entire month, and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly average of daily mean temperatures, then plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Shades of blue show areas that had monthly average temperatures below 50°F. The darker the shade of blue, the lower the average temperature. Areas shown in shades of orange and red had average temperatures above 50°F. The darker the shade of orange or red, the higher the average temperature. White or very light colors show areas where the average temperature was near 50°F. Q: Why do these data matter? A: The 5x5km NClimGrid data allow scientists to report on recent temperature conditions and track long-term trends at a variety of spatial scales. The gridded cells are used to create statewide, regional and national snapshots of climate conditions. Energy companies use this information to estimate demand for heating and air conditioning. Agricultural businesses also use these data to optimize timing of planting, harvesting, and putting livestock to pasture. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Average Temperature References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions) NCEI Monthly National Analysis) Climate at a Glance - Data Information) NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-us-monthly-averageThis upload includes two additional files:* Temperature - US Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots.* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

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

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

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

Temperature in the United States increased to 10.25 celsius in 2023 from 9.74 celsius in 2022. This dataset includes a chart with historical data for the United States Average Temperature.

This site is a Data Snapshots Image Gallery from Climate.Gov, featuring 12 monthly images of temperature maps of the continental United States showing the minimum temperature. Description:Q:How cool does it usually get overnight during this month?A:Based on daily observations from 1991-2020, colors on the map show the long-term average minimum temperature, sometimes referred to as “the overnight low,” in 5x5 km grid cells for the month displayed. The map reveals the average of overnight low temperatures during the month over the previous three decades.Q:Where do these measurements come from?A:Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers and automated instruments collected the lowest temperature at each station every day from 1991 to 2020, and sent them to the National Centers for Environmental Information (NCEI). After scientists checked the quality of the data to omit any systematic errors, they calculated each station’s average monthly minimum temperature by taking the sum of all the daily minimum temperatures for a month (for example all Junes from 1991-2020)) and dividing it by the total number of daily measurements (the number of days in the month times 30 years). NCEI scientists then plotted the values on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolated (or estimated) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Q:What do the colors mean?A:The color in each 5x5 km grid cell shows the average of the lowest temperature recorded every day of the month for the 30 years from 1991 to 2020. Shades of blue show where the lowest daily temperatures measured from 1991 to 2020 averaged below 50°F for the month. The darker the shade of blue, the lower the temperature. Areas shown in shades of orange and red have long-term average minimum temperatures above 50°F. The darker the shade of orange or red, the higher the temperature. White or very light colors show areas where the average minimum temperature is near 50°F.Q:Why do these data matter?A:Understanding these values provides insight into the “normal” conditions for a month. This type of information is widely used across an array of planning activities, from designing energy distribution networks, to the timing of crop and plant emergence, to choosing the right place and time for recreational activities.Q:How did you produce these snapshots?A:Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on NClimGrid climate data produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps.Additional informationThe data used in these snapshots can be downloaded from different places and in different formats. We used this specific data source:NClimGrid Temperature Normals

In 2024, the average annual temperature in the United States was 13.06 degrees Celsius, the warmest year recorded in the period in consideration. In 1895, this figure stood at 10.18 degrees Celsius. Recent years have been some of the warmest years recorded in the country.

The North America climate data were derived from WorldClim, a set of global climate layers developed by the Museum of Vertebrate Zoology at the University of California, Berkeley, USA, in collaboration with The International Center for Tropical Agriculture and Rainforest CRC with support from NatureServe.The global climate data layers were generated through interpolation of average monthly climate data from weather stations across North America. The result is a 30-arc-second-resolution (1-Km) grid of mean temperature values. The North American data were clipped from the global data and reprojected to the standard Lambert Azimuthal Equal Area projection used for the North American Environmental Atlas. Background information on the WorldClim database is available in: Very High-Resolution Interpolated Climate Surfaces for Global Land Areas; Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis; International Journal of Climatology 25: 1965-1978; 2005.Files Download

Q: What average temperatures are projected for future decades if global emissions of heat-trapping gases continue increasing through 2100? A: Colors show projected average daily temperature for each month from the 2020s through the 2090s, based on a high-emissions future. In this case, the high-emissions future represents a specific Representative Concentration Pathway (RCP) called RCP 8.5. Learn more about RCPs » « Go back to the Data Snapshots interface Q: Where do these measurements come from? A: Temperature projections in these images represent output from 32 global climate models that are all part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Projections labeled as “High emissions” represent a potential future in which global emissions continue increasing through the 21st century. By 2100, the result of this pathway is climate forcing of 8.5 Watts per square meter at the top of the atmosphere. Based on the energy imbalance along this pathway, global climate models calculate temperature across Earth’s surface for future periods. The RCP 8.5 scenario represents a future in which no climate policies are enacted. To produce regionally relevant projections, results from the global models were statistically downscaled using a method called Localized Constructed Analogs (LOCA). This technique uses observed local-scale weather and climate information to increase the spatial resolution of global-scale projections, and corrects for bias in the model simulations. Images of long-term averages from 1981 to 2010 (PRISM normals) show recent conditions; these maps provide a baseline for comparison with future projections. To produce the normals data, the PRISM group at Oregon State University gathered temperature and precipitation records from a range of federal, state, and international weather station networks, and then mapped them to a grid. To fill map areas between observation stations, the group used a digital elevation model as a predictor grid, and refined the model to account for local effects of mountains, distance from coasts, and other factors that affect climate in complex terrains. Q: What do the colors mean? A: Shades of blue show where average temperature for the month was, or is projected to be, below 60°F during the period indicated. The darker the shade of blue, the lower the temperature. Areas shown in shades of orange and red had, or are projected to have, average temperatures over 60°F. The darker the shade of orange or red, the higher the temperature. White or very light colors show where the average temperature was, or is projected to be, near 60°F. Q: Why do these data matter? A: In order to meet future needs for energy, food, and public health, planners and other decision makers need to understand how temperatures are projected to change over the coming decades. As the climate system continues responding to the heat-trapping gases we have added to the atmosphere, temperatures will change at different rates in different regions. These images can help people get a sense of how much warming their region will experience each decade so they can plan ahead for new conditions. These data also provide people with a way to compare conditions projected for stabilized emissions with conditions projected for high emissions. Comparing the two potential futures may encourage people to take actions to reduce emissions. Q: How did you produce these snapshots? A: We used a suite of Python scripts to process and visualize LOCA (Localized Constructed Analogs) data. The processing scripts averaged the daily values for each month in a given decade from all 32 global climate models that comprise the LOCA dataset. We then calculated the median of all models in each month of the decade. The visualization scripts produced maps of the results within the contiguous United States. For further information, see the README file or access the scripts on GitHub ». Add

This map layer depicts the climate zone designations used by the U.S. Department of Energy Building America Program by county boundaries (generalized version). It is intended as an aid in helping builders to identify the appropriate climate designation for the counties in which they are building. The guide can be used in conjunction with guidance in the Building America Solution Center and the Best Practices builders’ guides produced by the DOE Building America Program to help builders determine which climate-specific guidance they should use. This data for this layer is taken from Building America Best Practices Series, Volume 7.3 - Guide to Determining Climate Regions by County. The eight U.S. Building America climate regions described here are based on the climate designations used by the International Energy Conservation Code (IECC) and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). The IECC climate zone map was developed by DOE researchers at Pacific Northwest National Laboratory with input from Building America team members, in particular Joseph Lstiburek of Building Science Corporation.a,b The IECC map was developed to provide a simplified, consistent approach to defining climate for implementation of various codes; it was based on widely accepted classifications of world climates that have been applied in a variety of different disciplines. The PNNL-developed map was adopted by the IECC and was first included in the IECC in the 2004 Supplement to the IECC. It first appeared in ASHRAE 90.1 in the 2004 edition. The IECC map divided the United States into eight temperatureoriented climate zones. These zones are further divided into three moisture regimes designated A, B, and C. Thus the IECC map allows for up to 24 potential climate designations. In 2003, with direction from the Building America teams, researchers at DOE’s National Renewable Energy Laboratory simplified the IECC map for purposes of the Building America Program, into eight climate zones. For reporting purposes, these are further combined into five climate categories: Hot-humid,hot-dry/mixed drymixed-humidmarinecold/very coldsubarctic.The Building America and IECC climate maps are shown in Figures 1 and 2. The climate regions are described below. Climate zone boundaries follow county boundary lines. A listing of counties comprising each climate zone is provided below, beginning on page 5. The climate region definitions are based on heating degree days, average temperatures, and precipitation as follows:Hot-HumidA hot-humid climate is defined as a region that receives more than 20 inches (50 cm) of annual precipitation and where one or both of the following occur:• A 67°F (19.5°C) or higher wet bulb temperature for 3,000 or more hours during the warmest six consecutive months of the year; or• A 73°F (23°C) or higher wet bulb temperature for 1,500 or more hours during the warmest six consecutive months of the year.The Building America hot-humid climate zone includes the portions of IECC zones 1, 2, and 3 that are in the moist category (A) below the “warm-humid” line shown on the IECC map. Mixed-HumidA mixed-humid climate is defined as a region that receives more than 20 inches (50 cm) of annual precipitation, has approximately 5,400 heating degree days (65°F basis) or fewer, and where the average monthly outdoor temperature drops below 45°F (7°C) during the winter months.The Building America mixed-humid climate zone includes the portions of IECC zones 4 and 3 in category A above the “warmhumid” line. Hot-DryA hot-dry climate is defined as a region that receives less than 20 inches (50 cm) of annual precipitation and where the monthly average outdoor temperature remains above 45°F (7°C) throughout the year.The Building America hot-dry climate zone corresponds to the portions of IECC zones 2 and 3 in the dry category.Mixed-Dry A mixed-dry climate is defined as a region that receives less than 20 inches (50 cm) of annual precipitation, has approximately 5,400 heating degree days (65°F basis) or less, and where the average monthly outdoor temperature drops below 45°F (7°C) during the winter months.The Building America mixed-dry climate zone corresponds to IECC climate zone 4 B (dry).Cold A cold climate is defined as a region with between 5,400 and 9,000 heating degree days (65°F basis).The Building America cold climate corresponds to the IECC climate zones 5 and 6.Very-Cold A very cold climate is defined as a region with between 9,000 and 12,600 heating degree days (65°F basis).The Building America very cold climate corresponds to IECC climate zone 7.SubarcticA subarctic climate is defined as a region with 12,600 heating degree days (65° basis) or more. The only subarctic regions in the United States are in found Alaska, which is not shown in Figure 1.The Building America subarctic climate zone corresponds to IECC climate zone 8.Marine A marine climate is defined as a region that meets all of the following criteria: • A coldest month mean temperature between 27°F (-3°C) and 65°F (18°C)• A warmest month mean of less than 72°F (22°C)• At least 4 months with mean temperatures higher than 50°F (10°C)• A dry season in summer. The month with the heaviest precipitation in the cold season has at least three times as much precipitation as the month with the least precipitation in the rest of the year. The cold season is October through March in the Northern Hemisphere and April through September in the Southern Hemisphere.The Building America marine climate corresponds to those portions of IECC climate zones 3 and 4 located in the “C” moisture category. Building America and IECC Climate ZonesThe table below shows the relationship between the Building America and IECC climate zones.

Building America
IECC


Subarctic
Zone 8


Very Cold
Zone 7


Cold
Zone 5 and 6


Mixed-Humid
4A and 3A counties above warm-humid line


Mixed-Dry
Zone 4B


Hot-Humid
2A and 3A counties below warm-humid line


Hot-Dry
Zone 3B


Marine
All counties with a “C” moisture regime

This statistic shows cities in the United States with the highest average annual temperatures. Data is based on recordings from 1981 to 2010. In San Antonio, Texas the average temperature is 80.7 degrees Fahrenheit. Some cities that have the hottest maximum summer temperatures will not be included in this list due to their extreme temperature variance.

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

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

Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al. (https://doi.org/10.1029/2007EO470006) at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones. Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes.�Plant hardiness zones provide a general indication of the extent of overwinter stress experienced by plants. PHZ are based on the average annual extreme minimum temperatures and have been used by horticulturists to evaluate the cold hardiness of plants. Specifically, the value used here is the absolute minimum temperature achieved for each year and reported as the 30-year mean. Because they reflect cold tolerance for many plant species, including woody ones, hardiness zones are most likely to reflect plant range limits. The zonal variations caused by warming temperatures in the future will therefore be useful to approximately delineate niche constraints of many plant species and hence their future range potential. Plant hardiness zones and subzones were delineated according to the USDA definitions, which break the geography into zones by 10 �F (5.56 �C) increments from zone 1 (-55 to -45.6 �C) to zone 13 (15.7 to 22 �C) of annual extreme minimum temperature. To define the coldest day per year, daily minimum temperatures were identified within the period July 1 to June 30, with the nominal year assigned to the first 6 months of the 12-month period.�Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Q: What average temperatures are projected for the future if we reduce and stabilize global emissions of heat-trapping gases within the next two decades? A: Colors show projected daily average temperature for each month from the 2020s through the 2090s, based on a stabilized-emissions future. In this case, the stabilized-emissions future represents a specific Representative Concentration Pathway (RCP) called RCP 4.5. Learn more about RCPs » « Go back to the Data Snapshots interface Q: Where do these measurements come from? A: Temperature projections in these images represent output from 32 global climate models that are all part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Projections labeled as “Stabilized emissions” represent a potential future in which global emissions peak around 2040, and then are reduced and stabilized. By 2100, the result of this pathway is climate forcing of 4.5 Watts per square meter at the top of the atmosphere. Based on the energy imbalance along this pathway, global climate models calculate temperature across Earth’s surface for future periods. The RCP 4.5 scenario is associated with warming of approximately 2°C above the modern climate normal. To produce regionally relevant projections, results from the global models were statistically downscaled using a method called Localized Constructed Analogs (LOCA). This technique uses observed local-scale weather and climate information to increase the spatial resolution of global-scale projections, and corrects for bias in the model simulations. Images of long-term averages from 1981 to 2010 (PRISM normals) show recent conditions; these maps provide a baseline for comparison with future projections. To produce the normals data, the PRISM group at Oregon State University gathered temperature and precipitation records from a range of federal, state, and international weather station networks, and then mapped them to a grid. To fill map areas between observation stations, the group used a digital elevation model as a predictor grid, and refined the model to account for local effects of mountains, distance from coasts, and other factors that affect climate in complex terrains. Q: What do the colors mean? A: Shades of blue show where average maximum temperature for the month was, or is projected to be, below 60°F during the period indicated. The darker the shade of blue, the lower the temperature. Areas shown in shades of orange and red had, or are projected to have, average maximum temperatures over 60°F. The darker the shade of orange or red, the higher the temperature. White or very light colors show where the average maximum temperature was, or is projected to be, near 60°F. Q: Why do these data matter? A: In order to meet future needs for energy, food, and public health, planners and other decision makers need to understand how temperatures are projected to change over the coming decades. As the climate system continues responding to the heat-trapping gases we have added to the atmosphere, temperatures will change at different rates in different regions. These images can help people get a sense of how much warming their region will experience each decade so they can plan ahead for new conditions. These data also provide people with a way to compare conditions projected for stabilized emissions with conditions projected for high emissions. Comparing the two potential futures may encourage people to take actions to reduce emissions. Q: How did you produce these snapshots? A: We used a suite of Python scripts to process and visualize LOCA (Localized Constructed Analogs) data. The processing scripts averaged the daily values for each month in a given decade from all 32 global climate models that comprise the LOCA dataset. We then calculated the median of all models in each month of the decade. The visualization scripts produced maps of the results within the contiguous United States. For further information, see the README file or access the scripts on GitHu

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

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

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

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

Q: How much rain and snow fell through the month? A: Colors show monthly precipitation totals across the contiguous United States. The darker the color, the higher the total precipitation. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total precipitation and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). Q: What do the colors mean? A: Areas shown in white received little or no measurable precipitation for the month. Areas shown in the lightest green received less than one inch of water from rain or snow. The darker the color on the map, the higher the precipitation for the month. Areas shown in dark blue received eight inches or more of precipitation that fell as either rain or snow. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Farmers and gardeners who depend on rain for their plants want to know if enough precipitation has fallen to support plant growth. Similarly, forest managers and ranchers check monthly precipitation to monitor the status of the environment. Water managers who work to ensure that towns and cities have enough water for drinking, washing, and industrial uses are also interested in how much precipitation falls each month. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Q: Data Format Description A: NetCDF (Version: 4) Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total This upload includes two additional files:* Precipitation - Monthly Total _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/precipitation-monthly-total )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Numbered series of NOAA publications that contain environmental information climate summaries and station normals. Each series contains a volume for each state, climate division, or station. Series that are or would be found in this library are as follows: -- No. 10: Climatic Summary of the United States, establishment of stations to 1930, by climate division -- No. 11: Climatic Summary of the United States-Supplement for 1931-52, by state -- No. 20: Monthly Station Climate Summaries for 1971-2000. Station summaries of particular interest to agriculture, industry, and engineering applications. These summaries contain a variety of statistics for temperature, precipitation, snow, freeze dates, and degree day elements for 4,273 stations. -- No. 20: Station Climatological Summaries, station list through 1985. -- No. 20: Supplement 1: Frost/Freeze Data. -- No. 30: Summary of Hourly Observations, data 1949-55 (varies), by station. -- No. 60: Climates of the States, published 1960, by state. -- No. 81: Decennial Census of United States Climate - Monthly Normals of Temperature, Precipitation, and Heating and Cooling Degree Days, by state, reused for 30 year periods 1931-60 thru 1971-2000, by station. -- No. 81: Supplement 1: Monthly Precipitation Probabilities. -- No. 81: Supplement 2: Annual Degree Days to Selected Bases. -- No. 82: Decennial Census of United States Climate - Summary of Hourly Observations 1951-1960, by station. -- No. 84: Daily Station Normals of Temperature, Precipitation and Heating and Cooling Degree Days, by station. -- No. 85: Monthly Divisional Normals and Standard Deviations of Temperature, Precipitation, and Heating and Cooling Degree Days, by climate division. -- No. 86: Decennial Census of United States Climate - Climatic Summary of the U.S. 1951-1960, by state. -- No. 90: Airport Climatological Summary, 1965-1974, by station.