The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in August 2025, the average temperature across the North American country stood at 22.98 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 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.

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.

This feature layer contains the gridded one month PRISM Temperature Normals from Oregon State University on a 0.5 x 0.5 degree grid for the contiguous United States. The data was originally created in February 2018. These climatologies will be updated along with the drought outlook tools.The one month climatology has the same time period as the one month lead for the Climate Prediction Center's One Month Outlook. This climatology is for the current one month forecast released on the third Thursday of every month and updated on the last day of the month for the following month. This is a tool for the Drought Outlook Interactive Web Map and Drought Outlook Interactive Experience.The Climate Prediction Center uses climatologies with a base period from 1981 to 2010.For more information visit the PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu

Q: Was the month cooler or warmer than usual? A: Colors show where and by how much the monthly average temperature differed from the month’s long-term average temperature from 1991-2020. Red areas were warmer than the 30-year average for the month, and blue areas were cooler. White and very light areas had temperatures close to the long-term average. 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). To calculate the difference-from-average temperatures shown on these maps—also called temperature anomalies—NCEI scientists take the average temperature in each 5x5 km grid box for a single month and year, and subtract its 1991-2020 average for the same month. If the result is a positive number, the region was warmer than average. A negative result means the region was cooler than usual. Q: What do the colors mean? A: Shades of blue show places where average monthly temperatures were below their long-term average for the month. Areas shown in shades of pink to red had average temperatures that were warmer than usual. The darker the shade of red or blue, the larger the difference from the long-term average temperature. White and very light areas show where average monthly temperature was the same as or very close to the long-term average. Q: Why do these data matter? A: Comparing an area’s recent temperature to its long-term average can tell how warm or how cool the area is compared to usual. Temperature anomalies also give us a frame of reference to better compare locations. For example, two areas might have each had recent temperatures near 70°F, but 70°F could be above average for one location while below average for another. Knowing an area is much warmer or much cooler than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress, estimate energy use, consider snow and lake ice melt; and to understand impacts on wildfire regimes. 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. 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 Average Temperature NClimGrid Temperature Normals 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 Cl

Temperature in the United States increased to 10.73 celsius in 2024 from 10.25 celsius in 2023. This dataset includes a chart with historical data for the United States Average Temperature.

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.

Dataset consists of twelve monthly images for 1991-2020, available in small, large, broadcast media, full size zip, and KML archive formats. These images were derived from NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid).Description from Climate.govQ:What is the long-term average temperature for this month?A:Based on daily observations from 1991-2020, colors on the map show the long-term average mean temperature in 5x5 km grid cells for the month displayed. The maps show mean temperatures—the arithmetic average between the highest and lowest temperature in a 24-hour period—averaged together over the month for 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 highest and 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 monthly average of daily mean temperatures, 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 highest and lowest temperature recorded every day of the month for the 30 years from 1991 to 2020. Shades of blue show where the mean 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 mean 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 mean 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 NormalsReferencesNOAA 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

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 data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. 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 that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean 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 month going back to 1880. Be sure to configure the time settings in your web map to view the time series 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. A version showing just the most recent month is available here.

This is a map of average temperatures in 2024 at COOP sites across western and north-central NY. 2024 was the warmest on record for several sites. Thank you to all our COOP observers.

US Weather History

Actual and Average Temperatures and Precipitation

By FiveThirtyEight [source]

About this dataset

This dataset contains a collection of weather data from twelve major cities across the United States, including Los Angeles (KCTQ), Charlotte (KCLT), Houston (KHOU), Indianapolis (KIND), Jacksonville (KJAX), Chicago (KMDW), New York City (KNYC), Philadelphia(KPHL ), Phoenix( KPHX) and Seattle( KSEA). These datasets offer an exciting insight into the changing temperatures and climate in these key locations over a period of 12 months. Whether you are an experienced researcher in climate science or just interested in understanding more about world weather trends, this dataset provides an invaluable source.

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How to use the dataset

This dataset contains 12 weather records from various cities across the US, from Los Angeles to New York City. Each record includes information about average and actual temperatures, as well as precipitation and related records.

Research Ideas

• Using the data to map out a timeline of high temperature records throughout the US and compare it to predictions of climate scientists on how climate change will affect regional temperatures in a given area.
• Tracking average and actual precipitation levels over the course of an entire year in various cities around the US in order to develop city-specific estimates for water resource availability in future years.
• Comparing record temperatures across cities in different regions, determining if there are any correlations between geographical location and temperature extremes, and then extrapolating these findings to better understand local weather patterns on both short-term or long-term scales

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: KPHL.csv | Column name | Description | |:--------------------------|:--------------------------------------------------------------------| | date | The date of the weather record. (Date) | | actual_mean_temp | The actual mean temperature for the day. (Float) | | actual_min_temp | The actual minimum temperature for the day. (Float) | | actual_max_temp | The actual maximum temperature for the day. (Float) | | average_min_temp | The average minimum temperature for the day. (Float) | | average_max_temp | The average maximum temperature for the day. (Float) | | record_min_temp | The record minimum temperature for the day. (Float) | | record_max_temp | The record maximum temperature for the day. (Float) | | record_min_temp_year | The year in which the record minimum temperature was set. (Integer) | | record_max_temp_year | The year in which the record maximum temperature was set. (Integer) | | actual_precipitation | The actual precipitation for the day. (Float) | | average_precipitation | The average precipitation for the day. (Float) | | record_precipitation | The record precipitation for the day. (Float) |

File: KPHX.csv | Column name | Description | |:--------------------------|:--------------------------------------------------------------------| | date | The date of the weather record. (Date) | | actual_mean_temp | The actual mean temperature for the day. (Float) | | actual_min_temp | The actual minimum temperature for the day. (Float) | | actual_max_temp | The actual maximum temperature for the day. (Float) | | average_min_temp | The average minimum temperature for the day. (Float) | | average_max_temp | The average maximum temperature for the day. (Float) | | **record_min_...

England's highest monthly mean air temperatures are typically recorded in July and August of each year. Since 2015, the warmest mean temperature was measured in July 2018 at 18.8 degrees Celsius. On the other hand, February of that same year registered the coolest temperature, at 2.6 degrees Celsius. In September 2025, the mean air temperature was 13.8 degrees Celsius, matching the figure recorded the same month the previous year. The English weather England is the warmest region in the United Kingdom and the driest. In 2024, the average annual temperature in England amounted to 10.73 degrees Celsius – around 1.1 degrees above the national mean. That same year, precipitation in England stood at about 1,020 millimeters. By contrast, Scotland – the wettest region in the UK – recorded over 1,500 millimeters of rainfall in 2024. Temperatures on the rise Throughout the last decades, the average temperature in the United Kingdom has seen an upward trend, reaching a record high in 2022. Global temperatures have experienced a similar pattern over the same period. This gradual increase in the Earth's average temperature is primarily due to various human activities, such as burning fossil fuels and deforestation, which lead to the emission of greenhouse gases. This phenomenon has severe consequences, including more frequent and intense weather events, rising sea levels, and adverse effects on human health and the environment.

Q: Is annual sea surface temperature warmer or cooler than usual? A: Colors on this map show where and by how much annual sea surface temperature differed from a long-term average (1985-1993, details from Coral Reef Watch). Red and orange areas were warmer than average, and blue areas were cooler than average. The darker the color, the larger the difference from the long-term average. White and very light areas were near average. Q: Where do these measurements come from? A: Monthly measurements are made from NOAA's CoralTemp sea surface temperature (SST) data. Every day, instruments on eight satellites in two different orbits (geostationary and polar) measure sea surface temperature by checking how much energy is radiated by the ocean at different wavelengths. Computer programs plot these measurements on a gridded map and then merge and smooth the data into a gap-free product using mathematical filters. Each grid point covers an area approximately 5 x 5 km. Daily temperatures at each grid point are averaged together to calculate monthly average temperature. To calculate the difference-from-average temperatures shown here, a computer program takes the monthly average temperature at each grid point, and subtracts the long-term average for that month. Monthly measurements are averaged together to generate an annual image. If the result is a positive number, the sea surface was warmer than the long-term average. A negative result from the subtraction means the sea surface was cooler than usual. Q: What do the colors mean? A: Shades of blue show locations where sea surface temperature was cooler than its long-term average. Locations shown in shades of orange and red are where the sea’s surface was warmer than the long-term average. The darker the shade of red or blue, the larger the difference from the long-term average or “usual” sea surface temperature. Locations that are white or very light show where sea surface temperature was the same as or very close to its long-term average. Q: Why do these data matter? A: Water covers more than 70% of our planet's surface, so gathering data on ocean temperatures gives us a better picture of global temperatures. Tracking the temperature of the sea’s surface helps scientists understand how much heat energy is in the ocean and how it changes over time. Sea surface temperatures can have dramatic impacts on weather, including weather patterns such as El Niño-Southern Oscillation (ENSO) that travel hundreds of miles inland. Sea surface temperatures also play a significant role in the extent and thickness of Arctic and Antarctic sea ice, which serve as our planet’s built-in air-conditioning system. And sea surface temperatures have significant effects on marine life. The upwelling of cold water, for instance, provides nutrients to phytoplankton, the base of the marine food chain. In contrast, warm ocean surface waters deprive phytoplankton of nutrients, sometimes with devastating effects up the chain. 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. NOAA's Environmental Visualization Laboratory (NNVL) produces the Sea Surface Temperature Anomaly files. To produce our images, we run a set of scripts that access these NNVL source files, re-project them into a Hammer-Aitoff globe, and output them in a range of sizes. References NOAA NNVL Sea Surface Temperature Anomaly (SSTA) NOAA NNVL SSTA FTP access NOAA Coral Reef Watch CoralTemp data CoralTemp climatology (long-term average) CoralTemp climatology methodology Source: https://www.climate.gov/maps-data/data-snapshots/data-source/sst-global-yearly-difference-average This upload includes two additional files:* SST - Global, Yearly Difference from Average _NOAA Climate.gov.pdf is a scre

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. The data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. 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 that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean 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 the past month. Analysis: The full archive from 1880 – present is available here, and can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses.

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

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 U.S. Monthly Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). In addition to the standard set of normals, users also can find "agricultural normals", which are used in many industries, including but not limited to construction, architecture, pest control, etc. These supplemental "agricultural normals" include frost-freeze date probabilities, growing degree day normals, probabilities of reaching minimum temperature thresholds, and growing season length normals. Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.MapData

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.fed.us/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.fed.us/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

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Record High Temperatures for US Cities

Clearly Defined Monthly Data

By Gary Hoover [source]

About this dataset

This dataset contains all the record-breaking temperatures for your favorite US cities in 2015. With this information, you can prepare for any unexpected weather that may come your way in the future, or just revel in the beauty of these high heat spells from days past! With record highs spanning from January to December, stay warm (or cool) with these handy historical temperature data points

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How to use the dataset

This dataset contains the record high temperatures for various US cities during the year of 2015. The dataset includes columns for each individual month, along with column for the records highs over the entire year. This data is sourced from www.weatherbase.com and can be used to analyze which cities experienced hot summers, or compare temperature variations between different regions.

Here are some useful tips on how to work with this dataset: - Analyze individual monthly temperatures - this dataset allows you to compare high temperatures across months and locations in order to identify which areas experienced particularly hot summers or colder winters.
- Compare annual versus monthly data - use this data to compare average annual highs against monthly highs in order to understand temperature trends at a given location throughout all four seasons of a single year, or explore how different regions vary based on yearly weather patterns as well as across given months within any one year; - Heatmap analysis - use this data plot temperature information in an interactive heatmap format in order to pinpoint particular regions that experience unique weather conditions or higher-than-average levels of warmth compared against cooler pockets of similar size geographic areas; - Statistically model the relationships between independent variables (temperature variations by month, region/city and more!) and dependent variables (e.g., tourism volumes). Use regression techniques such as linear models (OLS), ARIMA models/nonlinear transformations and other methods through statistical software such as STATA or R programming language;
- Look into climate trends over longer periods - adjust time frames included in analyses beyond 2018 when possible by expanding upon the monthly station observations already present within the study timeframe utilized here; take advantage of digitally available historical temperature readings rather than relying only upon printed reports

With these helpful tips, you can get started analyzing record high temperatures for US cities during 2015 using our 'Record High Temperatures for US Cities' dataset!

Research Ideas

• Create a heat map chart of US cities representing the highest temperature on record for each city from 2015.
• Analyze trends in monthly high temperatures in order to predict future climate shifts and weather patterns across different US cities.
• Track and compare monthly high temperature records for all US cities to identify regional hot spots with higher than average records and potential implications for agriculture and resource management planning

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: Highest temperature on record through 2015 by US City.csv | Column name | Description | |:--------------|:--------------------------------------------------------------| | CITY | Name of the city. (String) | | JAN | Record high temperature for the month of January. (Integer) | | FEB | Record high temperature for the month of February. (Integer) | | MAR | Record high temperature for the month of March. (Integer) | | APR | Record high temperature for the month of April. (Integer) | | MAY | Record high temperature for the month of May. (Integer) | | JUN | Record high temperature for the month of June. (Integer) | | JUL | Record high temperature for the month of July. (Integer) | | AUG | Record high temperature for the month of August. (Integer) | | SEP | Record high temperature for the month of September. (Integer) | | OCT | Record high temperature for the month of October. (Integer) | | ...