These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

The Dallesport Airport weather station in Washington state recorded the highest daily temperature in the U.S. Pacific Northwest region during the 2021 heatwave. This weather station reached record highs two days straight, on June 27 and June 28, at 115 and 118 degrees Fahrenheit, or some 46.1 and 47.8 degrees Celsius, respectively. Oregon topped off the ranking of leading five record highs by weather station, at 117 degrees Fahrenheit (some 47.2 degrees Celsius). These records were between two degrees and 17 degrees higher than previous records, with the Arlington, Oregon weather station having had the longest-standing previous daily record high.

Like politics, you might say that all climate is local. As researchers seek to help the public better understand climate and climate change, a sensible approach would include helping people know more about changes in their own backyards. High and low temperatures are something that all of us pay attention to each day; when they are extreme (flirting with or setting records) they generate tremendous interest, largely because of the potential for significant impacts on human health, the environment, and built infrastructure. Changes through time in record high and low temperatures (extremes) are also an important manifestation of climate change (Sect. 3.8 in Trenberth et al. 2007; Peterson et al. 2008; Peterson et al. 2012). Meehl et al. (2009) found that currently, about twice as many high temperature records are being set as low temperature records over the conterminous U.S. (lower 48 states) as a whole. As the climate warms further, this ratio is expected to multiply, mainly because when the whole temperature distribution for a location or region shifts, it changes the "tails" of the distribution (in the case of warming this means fewer extreme cold temperatures and more extreme hot temperatures; see Page 2, Figure ES.1 of Karl et al. 2008). The Meehl et al. (2009) findings were covered pretty well by the online media, but, as is the case for all types of scientifc studies, it's safe to say that most of the public are not aware of these basic findings, and they would benefit from additional ways to get climate extremes information for their own areas and assess it. One such way is the National Climatic Data Center's (NCDC) U.S. Records Look-Up page. But how do most people typically hear about their area's high and low temperature records? Likely via the evening news, when their local on-air meteorologist notes the high/low for the day at a nearby airport then gives the years when the all-time high and low for the date were set (perhaps not at that same airport). The year of the record is an interesting bit of information on its own but it doesn't do much to place things in context. What about the local history of record temperatures and how things may be changing? Here we present a daily temperature records data product that we hope will serve the scientist and non-scientist alike in exploring and analyzing high and low temperature records and trends at hundreds of locations across the U.S. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/climate/temp/us_recordtemps/dayrec.html

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.

Click here to be taken directly to the ClimRR PortalClimate change is increasing the complexity, intensity, and frequency of disasters. Understanding future climate conditions in cities and towns across the United States is necessary to prepare for future climate realities. To address this requirement, ClimRR — the Climate Risk and Resilience Portal — empowers individuals, governments, and organizations to examine simulated future climate conditions at mid- and end-of-century for a range of climate perils. ClimRR was developed by the Center for Climate Resilience and Decision Science (CCRDS) at Argonne National Laboratory in collaboration with AT&T and the United States Department of Homeland Security’s Federal Emergency Management Agency (FEMA).Example: Climate adaptation planning starts with understanding the types of climate-related hazards and risks a community will likely face in the future. ClimRR helps analysts and planners gain an understanding of local-scale future climate conditions and extremes for wind, precipitation and temperature for most of the United StatesTEMPERATURE (SEASONAL)Each climate model generates temperature readings every 3 hours, or 8 temperature readings per day. The maximum daily temperature refers to the highest of these 8 readings, which often occurs in the middle of the daytime and is comparable to the 'high temperature' in a daily weather forecast. Similarly, the minimum daily temperature refers to the lowest of these 8 readings, which often occurs overnight and is comparable to the 'low temperature' in a daily weather forecast. Argonne calculated the seasonal average of both the maximum and minimum daily temperatures; the seasons are segmented as winter (Dec, Jan, Feb), spring (March, April, May), summer (June, July, Aug), and autumn (Sep, Oct, Nov). These calculations involved extracting the highest temperature reading and lowest temperature reading for each individual day of a year (e.g., 2045) within a given time period/scenario (e.g., mid-century RCP4.5) and for a given climate model (e.g., CCSM). These daily high/low readings were then classified by season and used to calculate the seasonal average maximum or minimum daily temperature for that scenario's model year (e.g., the average max daily temperature for summer of 2045 using the CCSM model under RCP4.5). This process was repeated for each year within a given time period/scenario (e.g., 2046, 2047, and so forth) across all three climate models (CCSM, GFDL, and HadGEM). Finally, the 30 individual seasonal averages for a given time period/scenario were themselves averaged, producing a multi-model ensemble mean that represents the seasonal average of the maximum or minimum daily temperature for a given time period/scenario. CLIMATE SCENARIOSClimate scenarios are the set of conditions used as inputs to climate models to represent estimates of future greenhouse gas (GHG) concentrations in the atmosphere. Climate models then evaluate how these GHG concentrations affect future (projected) climate. The data layers presented in this portal include results from two selected future climate scenarios for two 10‐year periods, and a historical 10‐year period for comparison:RCP4.5: Representative Concentration Pathway 4.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions peak around 2040, then decline.RCP8.5: Representative Concentration Pathway 8.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions continue to rise throughout the 21st century.Historical: Climate model is based on historical conditions, with results for 1995 to 2004. DOWNSCALED CLIMATE MODELSA global climate model is a complex mathematical representation of the major climate system components (atmosphere, land surface, ocean, and sea ice), and their interactions. These models project climatic conditions at frequent intervals over long periods of time (e.g., every 3 hours for the next 50-100 years), often with the purpose of evaluating how one or more GHG scenarios (such as RCP4.5 or RCP8.5) will impact future climate. Most global climate models project patterns at relatively coarse spatial resolutions, using grid-cells ranging from 100km2 to 200km2.The climate data presented in this portal has been downscaled to a higher spatial resolution (12km2) in order to fill a growing need for risk analysis and resilience planning at the local level. The process used to downscale global climate model data in this online portal is called dynamical downscaling. This method applies the pre-existing outputs of a global climate model as inputs to a separate, high-resolution regional climate model throughout its simulation. Dynamical downscaling accounts for the physical processes and natural features of a region, as well as the complex interaction between these elements and global dynamics under a climate scenario.Argonne’s dynamical downscaling employs the Weather Research and Forecasting (WRF) model, which is a regional weather model for North America developed by the National Center for Atmospheric Research. Argonne then conducted three separate regional modeling runs applying input data from a different global climate model for each simulation. These global climate models are:CCSM: The Community Climate System Model (Version 4) is a coupled global climate model developed by the University Corporation for Atmospheric Research with funding from the National Science Foundation, the Department of Energy, and the National Aeronautics and Space Administration. It is comprised of atmospheric, land surface, ocean, and sea ice submodels that run simultaneously with a central coupler component.GFDL: The Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration developed the Earth System Model Version 2G (note: the general convention, which we use, is to use the Laboratory's abbreviation to identify this model). It includes an atmospheric circulation model and an oceanic circulation model, and takes into account land, sea ice, and iceberg dynamics.HadGEM: The United Kingdom’s Met Office developed the Hadley Global Environment Model 2—Earth System. It is used for both operational weather forecasting and climate research, and includes coupled atmosphere‐ocean analysis and an earth system component that includes dynamic vegetation, ocean biology, and atmospheric chemistry.Regional modeling with the global climate model outputs (i.e., dynamical downscaling) began by conducting a validation study, in which the WRF model is run using inputs from the global climate models over a historical period (in this case, 1995-2004). This 'backcasting' allows for an assessment of the WRF model's ability to reproduce observed local climate trends. Once validated, Argonne then supplied each individual global climate model's outputs (CCSM, GFDL, and HadGEM) for each climate scenario (mid-century RCP4.5, mid-century RCP8.5, end-of-century RCP4.5, and end-of-century RCP8.5) to the WRF regional model, producing three different downscaled projections of future climate conditions for each scenario, along with downscaled historical data for each global climate model. ENSEMBLE MEANSAll data layers represent a variable along with its associated time period and climate scenario (e.g., mid-century RCP4.5). Each time period comprises one decade's worth of information: the historical (1995 – 2004), the mid-century (2045 – 2054), or the end-of-century (2085 – 2094). For each time period/climate scenario, the WRF model is run with each of the three global climate model outputs, producing three individual decades of weather data for each time period. In other words, Argonne's climate modeling produces 30 years of climate data for each decadal time period/climate scenario. By using the outputs from three different global climate models, rather than a single model, Argonne’s climate projections better account for the internal uncertainty associated with any single model. Each year's worth of data includes weather outputs for every 3 hours, or 8 modeled outputs per day. While this allows for a high degree of granularity in assessing future climate trends, it can also lead to a number of different ways to analyze this data; however, there are several important base methodologies shared across all variables presented in this portal. Most variables are presented as annual or seasonal averages of daily observations; however, each annual/seasonal average draws upon all three different climate model runs for that time period/climate scenario, along with the ten years of data produced by each model run. Therefore, each variable (e.g., total annual precipitation) for a given time period/scenario (e.g., mid-century RCP4.5) is produced by calculating an individual estimate for each of the 30 years of climate data associated with that time period/scenario, and then taking the average of the 30 estimates. This result is what we term the ensemble mean.

The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2023. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.

This version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.

This dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

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

Click here to be taken directly to the ClimRR PortalClimate change is increasing the complexity, intensity, and frequency of disasters. Understanding future climate conditions in cities and towns across the United States is necessary to prepare for future climate realities. To address this requirement, ClimRR — the Climate Risk and Resilience Portal — empowers individuals, governments, and organizations to examine simulated future climate conditions at mid- and end-of-century for a range of climate perils. ClimRR was developed by the Center for Climate Resilience and Decision Science (CCRDS) at Argonne National Laboratory in collaboration with AT&T and the United States Department of Homeland Security’s Federal Emergency Management Agency (FEMA).Example: Climate adaptation planning starts with understanding the types of climate-related hazards and risks a community will likely face in the future. ClimRR helps analysts and planners gain an understanding of local-scale future climate conditions and extremes for wind, precipitation and temperature for most of the United StatesTEMPERATURE (ANNUAL)Each climate model generates temperature readings every 3 hours, or 8 temperature readings per day. The maximum daily temperature refers to the highest of these 8 readings, which often occurs in the middle of the daytime and is comparable to the 'high temperature' in a daily weather forecast. Similarly, the minimum daily temperature refers to the lowest of these 8 readings, which often occurs overnight and is comparable to the 'low temperature' in a daily weather forecast. Argonne calculated the annual average of both the maximum and minimum daily temperatures. These calculations involved extracting the highest temperature reading and lowest temperature reading for each individual day of a year (e.g., 2045) within a given time period/scenario (e.g., mid-century RCP4.5) and for a given climate model (e.g., CCSM). These daily high/low readings were then used to calculate the annual average maximum or minimum daily temperature for that scenario's model year (e.g., the average max daily temperature for 2045 using the CCSM model under RCP4.5). This process was repeated for each year within a given time period/scenario (e.g., 2046, 2047, and so forth) across all three climate models (CCSM, GFDL, and HadGEM). Finally, the 30 individual annual averages for a given time period/scenario were themselves averaged, producing a multi-model ensemble mean that represents the annual average of the maximum or minimum daily temperature for a given time period/scenario. CLIMATE SCENARIOSClimate scenarios are the set of conditions used as inputs to climate models to represent estimates of future greenhouse gas (GHG) concentrations in the atmosphere. Climate models then evaluate how these GHG concentrations affect future (projected) climate. The data layers presented in this portal include results from two selected future climate scenarios for two 10‐year periods, and a historical 10‐year period for comparison:RCP4.5: Representative Concentration Pathway 4.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions peak around 2040, then decline.RCP8.5: Representative Concentration Pathway 8.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions continue to rise throughout the 21st century.Historical: Climate model is based on historical conditions, with results for 1995 to 2004. DOWNSCALED CLIMATE MODELSA global climate model is a complex mathematical representation of the major climate system components (atmosphere, land surface, ocean, and sea ice), and their interactions. These models project climatic conditions at frequent intervals over long periods of time (e.g., every 3 hours for the next 50-100 years), often with the purpose of evaluating how one or more GHG scenarios (such as RCP4.5 or RCP8.5) will impact future climate. Most global climate models project patterns at relatively coarse spatial resolutions, using grid-cells ranging from 100km2 to 200km2.The climate data presented in this portal has been downscaled to a higher spatial resolution (12km2) in order to fill a growing need for risk analysis and resilience planning at the local level. The process used to downscale global climate model data in this online portal is called dynamical downscaling. This method applies the pre-existing outputs of a global climate model as inputs to a separate, high-resolution regional climate model throughout its simulation. Dynamical downscaling accounts for the physical processes and natural features of a region, as well as the complex interaction between these elements and global dynamics under a climate scenario.Argonne’s dynamical downscaling employs the Weather Research and Forecasting (WRF) model, which is a regional weather model for North America developed by the National Center for Atmospheric Research. Argonne then conducted three separate regional modeling runs applying input data from a different global climate model for each simulation. These global climate models are:CCSM: The Community Climate System Model (Version 4) is a coupled global climate model developed by the University Corporation for Atmospheric Research with funding from the National Science Foundation, the Department of Energy, and the National Aeronautics and Space Administration. It is comprised of atmospheric, land surface, ocean, and sea ice submodels that run simultaneously with a central coupler component.GFDL: The Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration developed the Earth System Model Version 2G (note: the general convention, which we use, is to use the Laboratory's abbreviation to identify this model). It includes an atmospheric circulation model and an oceanic circulation model, and takes into account land, sea ice, and iceberg dynamics.HadGEM: The United Kingdom’s Met Office developed the Hadley Global Environment Model 2—Earth System. It is used for both operational weather forecasting and climate research, and includes coupled atmosphere‐ocean analysis and an earth system component that includes dynamic vegetation, ocean biology, and atmospheric chemistry.Regional modeling with the global climate model outputs (i.e., dynamical downscaling) began by conducting a validation study, in which the WRF model is run using inputs from the global climate models over a historical period (in this case, 1995-2004). This 'backcasting' allows for an assessment of the WRF model's ability to reproduce observed local climate trends. Once validated, Argonne then supplied each individual global climate model's outputs (CCSM, GFDL, and HadGEM) for each climate scenario (mid-century RCP4.5, mid-century RCP8.5, end-of-century RCP4.5, and end-of-century RCP8.5) to the WRF regional model, producing three different downscaled projections of future climate conditions for each scenario, along with downscaled historical data for each global climate model. ENSEMBLE MEANSAll data layers represent a variable along with its associated time period and climate scenario (e.g., mid-century RCP4.5). Each time period comprises one decade's worth of information: the historical (1995 – 2004), the mid-century (2045 – 2054), or the end-of-century (2085 – 2094). For each time period/climate scenario, the WRF model is run with each of the three global climate model outputs, producing three individual decades of weather data for each time period. In other words, Argonne's climate modeling produces 30 years of climate data for each decadal time period/climate scenario. By using the outputs from three different global climate models, rather than a single model, Argonne’s climate projections better account for the internal uncertainty associated with any single model. Each year's worth of data includes weather outputs for every 3 hours, or 8 modeled outputs per day. While this allows for a high degree of granularity in assessing future climate trends, it can also lead to a number of different ways to analyze this data; however, there are several important base methodologies shared across all variables presented in this portal. Most variables are presented as annual or seasonal averages of daily observations; however, each annual/seasonal average draws upon all three different climate model runs for that time period/climate scenario, along with the ten years of data produced by each model run. Therefore, each variable (e.g., total annual precipitation) for a given time period/scenario (e.g., mid-century RCP4.5) is produced by calculating an individual estimate for each of the 30 years of climate data associated with that time period/scenario, and then taking the average of the 30 estimates. This result is what we term the ensemble mean.

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 Daily Air Temperature and Heat Index data available on CDC WONDER are county-level daily average air temperatures and heat index measures spanning the years 1979-2010. Temperature data are available in Fahrenheit or Celsius scales. Reported measures are the average temperature, number of observations, and range for the daily maximum and minimum air temperatures, and also percent coverage for the daily maximum heat index. Data are available by place (combined 48 contiguous states, region, division, state, county), time (year, month, day) and specified maximum and minimum air temperature, and heat index value. The data are derived from the North America Land Data Assimilation System (NLDAS) through NLDAS Phase 2, a collaboration project among several groups: the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Princeton University, the National Weather Service (NWS) Office of Hydrological Development (OHD), the University of Washington, and the NCEP Climate Prediction Center (CPC). In a study funded by the NASA Applied Sciences Program/Public Health Program, scientists at NASA Marshall Space Flight Center/ Universities Space Research Association developed the analysis to produce the data available on CDC WONDER.

This collective of data from reports from worldwide CLIMAT stations is based on original data that are routinely disseminated by the responsible national weather services. All of the time series stored in the DWD database are made available. This data is quality-checked by the DWD for the purpose of climatological or climate-related applications. Further information: https://opendata.dwd.de/climate_environment/CDC/observations_global/CLIMAT/monthly/qc/air_temperature_absolute_max/historical/BESCHREIBUNG_obsglobal_monthly_qc_air_temperature_absolute_max_historical_de.pdf

Click here to be taken directly to the ClimRR PortalClimate change is increasing the complexity, intensity, and frequency of disasters. Understanding future climate conditions in cities and towns across the United States is necessary to prepare for future climate realities. To address this requirement, ClimRR — the Climate Risk and Resilience Portal — empowers individuals, governments, and organizations to examine simulated future climate conditions at mid- and end-of-century for a range of climate perils. ClimRR was developed by the Center for Climate Resilience and Decision Science (CCRDS) at Argonne National Laboratory in collaboration with AT&T and the United States Department of Homeland Security’s Federal Emergency Management Agency (FEMA).Example: Climate adaptation planning starts with understanding the types of climate-related hazards and risks a community will likely face in the future. ClimRR helps analysts and planners gain an understanding of local-scale future climate conditions and extremes for wind, precipitation and temperature for most of the United StatesTEMPERATURE (SEASONAL)Each climate model generates temperature readings every 3 hours, or 8 temperature readings per day. The maximum daily temperature refers to the highest of these 8 readings, which often occurs in the middle of the daytime and is comparable to the 'high temperature' in a daily weather forecast. Similarly, the minimum daily temperature refers to the lowest of these 8 readings, which often occurs overnight and is comparable to the 'low temperature' in a daily weather forecast. Argonne calculated the seasonal average of both the maximum and minimum daily temperatures; the seasons are segmented as winter (Dec, Jan, Feb), spring (March, April, May), summer (June, July, Aug), and autumn (Sep, Oct, Nov). These calculations involved extracting the highest temperature reading and lowest temperature reading for each individual day of a year (e.g., 2045) within a given time period/scenario (e.g., mid-century RCP4.5) and for a given climate model (e.g., CCSM). These daily high/low readings were then classified by season and used to calculate the seasonal average maximum or minimum daily temperature for that scenario's model year (e.g., the average max daily temperature for summer of 2045 using the CCSM model under RCP4.5). This process was repeated for each year within a given time period/scenario (e.g., 2046, 2047, and so forth) across all three climate models (CCSM, GFDL, and HadGEM). Finally, the 30 individual seasonal averages for a given time period/scenario were themselves averaged, producing a multi-model ensemble mean that represents the seasonal average of the maximum or minimum daily temperature for a given time period/scenario. CLIMATE SCENARIOSClimate scenarios are the set of conditions used as inputs to climate models to represent estimates of future greenhouse gas (GHG) concentrations in the atmosphere. Climate models then evaluate how these GHG concentrations affect future (projected) climate. The data layers presented in this portal include results from two selected future climate scenarios for two 10‐year periods, and a historical 10‐year period for comparison:RCP4.5: Representative Concentration Pathway 4.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions peak around 2040, then decline.RCP8.5: Representative Concentration Pathway 8.5, with results provided for a mid-century period (2045 to 2054) and end-of-century period (2085 to 2094). In this scenario, human GHG emissions continue to rise throughout the 21st century.Historical: Climate model is based on historical conditions, with results for 1995 to 2004. DOWNSCALED CLIMATE MODELSA global climate model is a complex mathematical representation of the major climate system components (atmosphere, land surface, ocean, and sea ice), and their interactions. These models project climatic conditions at frequent intervals over long periods of time (e.g., every 3 hours for the next 50-100 years), often with the purpose of evaluating how one or more GHG scenarios (such as RCP4.5 or RCP8.5) will impact future climate. Most global climate models project patterns at relatively coarse spatial resolutions, using grid-cells ranging from 100km2 to 200km2.The climate data presented in this portal has been downscaled to a higher spatial resolution (12km2) in order to fill a growing need for risk analysis and resilience planning at the local level. The process used to downscale global climate model data in this online portal is called dynamical downscaling. This method applies the pre-existing outputs of a global climate model as inputs to a separate, high-resolution regional climate model throughout its simulation. Dynamical downscaling accounts for the physical processes and natural features of a region, as well as the complex interaction between these elements and global dynamics under a climate scenario.Argonne’s dynamical downscaling employs the Weather Research and Forecasting (WRF) model, which is a regional weather model for North America developed by the National Center for Atmospheric Research. Argonne then conducted three separate regional modeling runs applying input data from a different global climate model for each simulation. These global climate models are:CCSM: The Community Climate System Model (Version 4) is a coupled global climate model developed by the University Corporation for Atmospheric Research with funding from the National Science Foundation, the Department of Energy, and the National Aeronautics and Space Administration. It is comprised of atmospheric, land surface, ocean, and sea ice submodels that run simultaneously with a central coupler component.GFDL: The Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration developed the Earth System Model Version 2G (note: the general convention, which we use, is to use the Laboratory's abbreviation to identify this model). It includes an atmospheric circulation model and an oceanic circulation model, and takes into account land, sea ice, and iceberg dynamics.HadGEM: The United Kingdom’s Met Office developed the Hadley Global Environment Model 2—Earth System. It is used for both operational weather forecasting and climate research, and includes coupled atmosphere‐ocean analysis and an earth system component that includes dynamic vegetation, ocean biology, and atmospheric chemistry.Regional modeling with the global climate model outputs (i.e., dynamical downscaling) began by conducting a validation study, in which the WRF model is run using inputs from the global climate models over a historical period (in this case, 1995-2004). This 'backcasting' allows for an assessment of the WRF model's ability to reproduce observed local climate trends. Once validated, Argonne then supplied each individual global climate model's outputs (CCSM, GFDL, and HadGEM) for each climate scenario (mid-century RCP4.5, mid-century RCP8.5, end-of-century RCP4.5, and end-of-century RCP8.5) to the WRF regional model, producing three different downscaled projections of future climate conditions for each scenario, along with downscaled historical data for each global climate model. ENSEMBLE MEANSAll data layers represent a variable along with its associated time period and climate scenario (e.g., mid-century RCP4.5). Each time period comprises one decade's worth of information: the historical (1995 – 2004), the mid-century (2045 – 2054), or the end-of-century (2085 – 2094). For each time period/climate scenario, the WRF model is run with each of the three global climate model outputs, producing three individual decades of weather data for each time period. In other words, Argonne's climate modeling produces 30 years of climate data for each decadal time period/climate scenario. By using the outputs from three different global climate models, rather than a single model, Argonne’s climate projections better account for the internal uncertainty associated with any single model. Each year's worth of data includes weather outputs for every 3 hours, or 8 modeled outputs per day. While this allows for a high degree of granularity in assessing future climate trends, it can also lead to a number of different ways to analyze this data; however, there are several important base methodologies shared across all variables presented in this portal. Most variables are presented as annual or seasonal averages of daily observations; however, each annual/seasonal average draws upon all three different climate model runs for that time period/climate scenario, along with the ten years of data produced by each model run. Therefore, each variable (e.g., total annual precipitation) for a given time period/scenario (e.g., mid-century RCP4.5) is produced by calculating an individual estimate for each of the 30 years of climate data associated with that time period/scenario, and then taking the average of the 30 estimates. This result is what we term the ensemble mean.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

Daily temperatures across weather stations in Canada reached as high as 117 degrees Fahrenheit (some 47.2 degrees Celsius) during the heatwave of 2021. These record highs were recorded in Kamloops and Oliver on the last days of June. Moreover, both cities had daily record highs for four consecutive days, from June 28 to July 1, 2021.

Year: year MeanLayDate: mean Julian date when first egg of the each clutch was laid ENSOWinter: mean ENSO score from December to March. Hurricanes: total number of hurricanes in the North Atlantic basin DaysBelow18_max: number of days with maximum daily temperature below 18.5 degrees Celsius or with rain during the 28 days after the mean fledging date. A crude measurement of weather conditions post fledging. TimePeriod: population trajectory at the time (growing, declining, post-decline)

DATA SOURCE: National Institute for Water and Atmospheric Research (NIWA) [Technical report available at https://www.mfe.govt.nz/publications/environmental-reporting/ministry-environment-atmosphere-and-climate-report-2020-updated]

Adapted by Ministry for the Environment and Statistics New Zealand to provide for environmental reporting transparency

This lowest aggregation dataset, was used to develop three ‘Our Atmosphere and Climate’ indicators. See Statistics New Zealand indicator links for specific methodologies and state/trend datasets (see ‘Shiny App’ downloads). 1) Temperature (https://www.stats.govt.nz/ndicators/temperature) 2) First and last frost days (https://www.stats.govt.nz/ndicators/frost-and-warm-days) 3) Growing degree days (https://www.stats.govt.nz/ndicators/growing-degree-days)

IMPORTANT INFORMATION Due to the size of this dataset (111 MB), a 32-bit version of Microsoft Excel will only display/download ~ 1 million rows. A DBMS, statistical or GIS application is needed to view the entire dataset.

This dataset shows two measures of temperature change in New Zealand: New Zealand’s national temperature from NIWA’s ‘seven-station’ temperature series from 1909 to 2019, and temperature at 30 sites around the country from at least 1972 to 2019. For national temperature, we report daily average, minimum and maximum temperatures. We also present New Zealand national and global temperature anomalies.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

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 dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario).

The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) to assist in the detection of regional climate change. Furthermore, it has been widely used in analyzing U.S. climte. The period of record varies for each station. USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage. Collaboration between NCDC and CDIAC on the USHCN project dates to the 1980s (Quinlan et al. 1987). At that time, in response to the need for an accurate, unbiased, modern historical climate record for the United States, the Global Change Research Program of the U.S. Department of Energy and NCDC chose a network of 1219 stations in the contiguous United States that would become a key baseline data set for monitoring U.S. climate. This initial USHCN data set contained monthly data and was made available free of charge from CDIAC. Since then it has been comprehensively updated several times [e.g., Karl et al. (1990) and Easterling et al. (1996)]. The initial USHCN daily data set was made available through CDIAC via Hughes et al. (1992) and contained a 138-station subset of the USHCN. This product was updated by Easterling et al. (1999) and expanded to include 1062 stations. In 2009 the daily USHCN dataset was expanded to include all 1218 stations in the USHCN.