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.

This dataset contains the average annual temperature (°F) for each year from 1900 to 2023. The data is based on observations from a network of thousands of weather stations across the United States.

Source: National Centers for Environmental Information (NCEI), a part of the National Oceanic and Atmospheric Administration (NOAA)

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, 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 mean annual temperature in North America stood at -4.5 degrees Celsius in 1995. It is expected that, 30 years later in 2025, the average temperature will increase by 1.6 degrees Celsius due to the effects of global warming, under a scenario where global temperatures increase by 1.5 degree Celsius.

The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

This metadata record describes the 30-year annual average of precipitation in millimeters (mm) and temperature (Celsius) during the period 1990–2019 for North America. The source data were produced by and acquired from DAYMET daily climate data (2020) and presented here as a series of two 1-kilometer resolution GeoTIFF files. An open source python code file used to process the data is also included.

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.

Temperature Over Time by State (Starts: 1895)

State and County Temperature Changes

By Environmental Data [source]

About this dataset

Do you want to know how rising temperatures are changing the contiguous United States? The Washington Post has used National Oceanic and Atmospheric Administration's Climate Divisional Database (nClimDiv) and Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) data sets to help analyze warming temperatures in all of the Lower 48 states from 1895-2019. To provide this analysis, we calculated annual mean temperature trends in each state and county in the Lower 48 states. Our results can be found within several datasets now available on this repository.

We are offering: Annual average temperatures for counties and states, temperature change estimates for each of the Lower 48-states, temperature change estimates for counties in the contiguous U.S., county temperature change data joined to a shapefile in GeoJSON format, gridded temperature change data for the contiguous U.S. in GeoTiff format - all contained with our dataset! We invite those curious about climate change to explore these data sets based on our analysis over multiple stories published by The Washington Post such as Extreme climate change has arrived in America, Fires, floods and free parking: California’s unending fight against climate change, In fast-warming Minnesota, scientists are trying to plant the forests of the future, This giant climate hot spot is robbing West of its water ,and more!

By accessing our dataset containing columns such as fips code, year range from 1895-2019, three season temperatures (Fall/Spring/Summer/Winter), max warming season temps plus temp recorded total yearly - you can become an active citizen scientist! If publishing a story or graphic work based off this data set please credit The Washington Post with a link back to this repository while sending us an email so that we can track its usage as well - 2cdatawashpost.com.

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How to use the dataset

The main files provided by this dataset are climdiv_state_year, climdiv_county_year, model_state, model_county , climdiv_national_year ,and model county .geojson . Each file contains different information capturing climate change across different geographies of the United States over time spans from 1895.

Research Ideas

• Investigating and mapping the temperatures for all US states over the past 120 years, to observe long-term changes in temperature patterns.
• Examining regional biases in warming trends across different US counties and states to help inform resource allocation decisions for climate change mitigation and adaption initiatives.
• Utilizing the ClimDiv National Dataset to understand continental-level average annual temperature changes, allowing comparison of global average temperatures with US averages over a long period of time

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: climdiv_state_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: climdiv_county_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: model_state.csv | Column name | Description | |:------------------...

• Parenthesis values are Celsius.

The dataset provides a comprehensive overview of the weather conditions across all cities of the world for a period of 12 months. It contains information on the average temperature in Celsius and Fahrenheit. This dataset is a valuable resource for researchers, meteorologists, and climate scientists who seek to understand the impact of climate change on different parts of the world. The data can be used to analyze trends in temperature, to develop predictive models for weather forecasting, and to evaluate the effectiveness of climate policies. The information in this dataset is updated regularly, ensuring that users have access to the most recent and accurate weather data available. With this dataset, users can gain valuable insights into the complex relationship between climate and the environment, and make informed decisions about climate change mitigation and adaptation strategies.

Description: ChatGPT

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

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean temp...

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.

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.

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

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.

Global Mean Temperature by Country (1950–2024)

Global Mean Temperature by Country (1950–2024) is a country-level climate dataset containing annual mean surface air temperatures for 196 countries from 1950 to 2024.

The dataset provides a consistent time series of national average temperatures, making it useful for climate analysis, global warming research, time-series modeling, and environmental data science.

Dataset Structure

Each row represents the average annual temperature for a specific country in a given year.

Columns:

• iso_code – ISO 3166-1 alpha-3 country code
• country – Country name
• year – Year of observation (1950–2024)
• mean_temperature – Mean annual surface air temperature in °C

Dataset Size

• Countries: 196
• Years: 1950–2024
• Total observations: 14,700
• Missing values: None

Potential Use Cases

This dataset can be used for:

• climate change research
• global temperature trend analysis
• time-series forecasting
• machine learning models
• environmental data visualization
• country-level climate comparisons

Source

Temperature values are derived from the CRU TS (Climatic Research Unit Time Series) dataset, which aggregates global weather station data to produce gridded climate observations.

Institution:

• Climatic Research Unit, University of East Anglia

Data accessed through:

• World Bank Climate Change Knowledge Portal

Mean annual temperature data layer used in the creation of Land Environments of New Zealand (LENZ) classification. The classification layers have been made publicly available by the Ministry for the Environment (see https://data.mfe.govt.nz/layers/?q=LENZ for to access these layers).

Mean annual temperature is recorded in °C. The climate station data used in the development of this climate surface were derived from summaries of climate observations published by the New Zealand Meteorological Service, using data collected over the period from 1950-1980. The resulting data layer was created by coupling a 100 m DEM with a thin-plate spline surface fitted to an irregular network of 300 meteorological stations. The resulting 100 metre layer was then interpolated to 25 metres using bilinear interpolation. Following conventions used in the calculation of climate summary statistics, the values used to fit the surface consisted of the mean of the 12 monthly averages for daily average temperature.

This layer has been multiplied by a factor of 10 (i.e. converted into an integer grid) to save space and make the grids more responsive. A value of 136 is actually 13.6 °C.

Additional details such as the climate station locations used in the creation of the layer and error maps are defined in the attached LENZ Technical Guide.

This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided.