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 1895. 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 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.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation. For instance, in January 2025, the average temperature across the North American country stood at -1.54 degrees Celsius. Rising temperatures Globally, 2015, 2016, 2019 and 2021 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.

Annual mean temperature is mean of the average temperatures for each month in degrees Celsius for the period of January 1971 through December 2009.The relationships established between species demographics and distributions with bioclimatic predictors can inform land managers of climatic effects on species during decision making processes.Dataset SummaryAnnual mean temperature was developed by the U.S. Geological Survey (USGS) as part of a collection Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States. These predictors highlight climate conditions best related to species physiology. The Parameter-elevation Regression on Independent Slopes Model (PRISM) and down-scaled PRISM data, which included both averaged multi-year and averaged monthly climate summaries, were used to develop these multi-scale bioclimatic predictors.Link to source metadataWhat can you do with this layer?The layer is restricted to an 24,000 x 24,000 pixel limit for these services, which represents an area roughly 1,200 miles on a side.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

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 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).

Annual average temperature and precipitation accumulation departures from climatology are plotted, using visualizations inspired by Ed Hawkins' Warming Stripes page. A temperature chart depicts years that are warmer (reds) and cooler (blue) than normal. In a similar fashion, precipitation graphs show wetter (greens) and drier (browns) conditions for a given year. Data is from 1895-present, using a climatology of 1901-2000. Alaska and Hawaii are not available.Description of DataData originates from NOAA NCEI's climate at a glance page, which uses a 5 kilometer gridded data set, known as nClimgrid. This data set provides temperature and precipitation information for each month back to 1895. Annual estimates since 1895 are derived from the monthly data and aggregated onto each county for the Contiguous United States (Alaska and Hawaii are not available at this time). To depict the long term change in temperature and precipitation, annual data are then compared to a 20th century average (1901-2000). (Note that this is different from Ed Hawkins' original project, which uses a 1971-2000 baseline. These differences in baseline mean that the graphics may not perfectly match: the general warming trends will be consistent). These differences from the century average (known as a departure from normal, or anomaly) are then used to produce the visual. For more information on anomalies, please refer to this FAQ page.This map is a copy of Jared Rennie's original map, published at https://arcg.is/19i1r90Data is from NOAA NCEI's climate at a glance page. Thanks to Ed Hawkins and Zeke Hausfather for inspiration. Plots and maps made by Jared Rennie (@jjrennie) Certified Consulting Meteorologist, North Carolina Institute for Climate Studies, Asheville, NC.

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.

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.

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 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

Context

Who among us doesn't talk a little about the weather now and then? Will it rain tomorrow and get so cold to shake your chin or will it make that cracking sun? Does global warming exist?

With this dataset, you can apply machine learning tools to predict the average temperature of Detroit city based on historical data collected over 5 years.

Content

The given data set was produced from the Historical Hourly Weather Data [https://www.kaggle.com/selfishgene/historical-hourly-weather-data], which consists of about 5 years of hourly measurements of various weather attributes (eg. temperature, humidity, air pressure) from 30 US and Canadian cities.

From this rich database, a cutout was made by selecting only the city of Detroit (USA), highlighting only the temperature, converting it to Celsius degrees and keeping only one value for each date (corresponding to the average daytime temperature - from 9am to 5pm).

In addition, temperature values ​​were artificially and gradually increased by a few Celsius degrees over the available period. This will simulate a small global warming (or is it local?)...

In summary, the available dataset contains the average daily temperatures (collected during the day), artificially increased by a certain value, for the city of Detroit from October 2012 to November 2017.

The purpose of this dataset is to apply forecasting models in order to predict the value of the artificially warmed average daily temperature of Detroit.

See graph in the following image: black dots refer to the actual data and the blue line represents the predictive model (including a confidence area).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3089313%2Faf9614514242dfb6164a08c013bf6e35%2Fplot-ts2.png?generation=1567827710930876&alt=media" alt="">

Acknowledgements

This dataset wouldn't be possible without the previous work in Historical Hourly Weather Data.

Inspiration

What are the best forecasting models to address this particular problem? TBATS, ARIMA, Prophet? You tell me!

This map plots the Change in Average Annual Temperature if Earth’s long-term average temperature reaches specific levels of warming. These Global Warming Levels (GWLs) correspond to global average temperature increases of 1.5, 2, 3, and 4 °C above pre-industrial levels measured from 1851 to 1900. On the Fahrenheit scale, these warming levels are 2.7, 3.6, 5.4, and 7.2 °F. As of the 2020s, global average temperature has already increased around 2 °F above pre-industrial levels.Each layer of the map is style with the same range of data so that the spatial patterns of change can be compared across all scenarios. The projections are derived from downscaled climate models from LOCA2 and STAR-ESDM, and were used in the 5th National Climate Assessment. Click on the layers below to view more detailed descriptions of how the data was generated.

In 2024, the maximum average temperature in Florida stood at 83 degrees Fahrenheit. The 'sunshine' state is hardly touched by low temperatures and even sees temperatures rise above 100 degrees statewide in the summer. For many of these hot states, maximum temperatures were above normal in 2024.

We provide temperature and anomaly data alongside Intergovernmental Panel on Climate Change (IPCC) global land temperature anomalies against the 1961 to 1990, and 1991 to 2020 baseline periods.

Global average temperatures have increased by around 1 degree Celsius in the last century, almost certainly a result of high levels of atmospheric greenhouse gases emitted from human activities. While this change may seem small, relatively small changes in our climate can have big effects on our environment (Ministry for the Environment [MfE] & Stats NZ, 2019).

Temperature change can have a significant effect on agriculture, energy demand, ecosystems, and recreation. Climate change projections for New Zealand suggest the greatest warming will be in summer/autumn and the least in winter and spring (MfE, 2018).

Temperature is also influenced by natural processes such as climate oscillations like the El Niño Southern Oscillation (ENSO). However, ENSO does not affect the long-term warming trend of the national temperature time series (World Meteorological Organization [WMO], 2014).

Variables: year: Year. temperature: Temperature in degrees Celsius. data_released: Year the data was released. source: Source of data. anomaly: Anomaly against the average temperature of a given reference period. reference_period: Reference period.

References: Ministry for the Environment. (2018). Climate Change Projections for New Zealand: Atmosphere Projections Based on Simulations from the IPCC Fifth Assessment, 2nd Edition (Publication No. ME 1385). https://www.mfe.govt.nz/publications/climate-change/climate-change-projections-new-zealand Ministry for the Environment & Stats NZ. (2019). New Zealand’s Environmental Reporting Series: Environment Aotearoa 2019 (Publication No. ME 1416). https://www.mfe.govt.nz/publications/environmental-reporting/environment-aotearoa-2019 World Meteorological Organization. (2014). El Niño/Southern Oscillation. WMO. (WMO-No. 1145). https://library.wmo.int/records/item/53800-el-nino-southern-oscillation

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

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).

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 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.

The US Global Change Research Program sponsors the semi-annual National Climate Assessment, which is the authoritative analysis of climate change and its potential impacts in the United States. The 4th National Climate Assessment (NCA4), issued in 2018, used high resolution, downscaled LOCA climate data for many of its national and regional analyses. The LOCA downscaling was applied to multi-model mean weighted averages, using the following 32 CMIP5 model ensemble:ACCESS1-0, ACCESS1-3, bcc-csm1-1, bcc-csm1-1-m, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC EARTH, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H-p1, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-M.All of the LOCA variables used in NCA4 are presented here. Many are thresholded to provide 47 actionable statistics, like days with precipitation greater than 3", length of the growing season, or days above 90 degrees F. Time RangesStatistics for each variables were calculated over a 30-year period. Four different time ranges are provided:Historical: 1976-2005Early-Century: 2016-2045Mid-Century: 2036-2065Late-Century: 2070-2099Climate ScenariosClimate models use estimates of greenhouse gas concentrations to predict overall change. These difference scenarios are called the Relative Concentration Pathways. Two different RCPs are presented here: RCP 4.5 and RCP 8.5. The number indicates the amount of radiative forcing(watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but RCP 4.5 aligns with the international targets of the COP-26 agreement, while RCP 8.5 is aligns with a more "business as usual" approach. Detailed documentation and the original data from USGCRP, processed by NOAA's National Climate Assessment Technical Support Unit at the North Carolina Institute for Climate Studies, can be accessed from the NCA Atlas. Variable DefinitionsCooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip Annual Total: Annual total precipitation [inches]Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip 1in: Annual number of days with total precipitation greater than 1 inchPrecip 2in: Annual number of days with total precipitation greater than 2 inchesPrecip 3in: Annual number of days with total precipitation greater than 3 inchesPrecip 4in: Annual number of days with total precipitation greater than 4 inchesPrecip Max 1 Day: Annual highest precipitation total for a single day [inches]Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]Daily Avg Temperature: Daily average temperature [degF]Daily Max Temperature: Daily maximum temperature [degF]Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005Days Above 100F: Annual number of days with a maximum temperature greater than 100degFDays Above 105F: Annual number of days with a maximum temperature greater than 105degFDays Above 110F: Annual number of days with a maximum temperature greater than 110degFDays Above 115F: Annual number of days with a maximum temperature greater than 115degFTemp Max 1 Day: Annual single highest maximum temperature [degF]Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]Days Above 86F: Annual number of days with a maximum temperature greater than 86degFDays Above 90F: Annual number of days with a maximum temperature greater than 90degFDays Above 95F: Annual number of days with a maximum temperature greater than 95degFTemp Min: Daily minimum temperature [degF]Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degFTemp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degFTemp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degFTemp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degFTemp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degFTemp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]Temp Min 1 Day: Annual single lowest minimum temperature [degF]Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]For For freeze-related variables:The first fall freeze is defined as the date of the first occurrence of 32degF or lower in the nine months starting midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The last spring freeze is defined as the date of the last occurrence of 32degF or lower in the nine months prior to midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The growing season is defined as the number of days between the last occurrence of 28degF/32degF/41degF or lower in the nine months prior to midnight August 1 and the first occurrence of 28degF/32degF/41degF or lower in the nine months starting August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 28degF/32degF/41degF or lower are excluded from the analysis.No freeze occurrence, value = 999