In 2024, Louisiana recorded 71.25 inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only 9.53 inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of 0.20 inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

Precipitation in the United States increased to 735.83 mm in 2023 from 707.98 mm in 2022. This dataset includes a chart with historical data for the United States Average Precipitation.

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

The majority of the wettest cities in the United States are located in the Southeast. The major city with the most precipitation is New Orleans, Louisiana, which receives an average of 1592 millimeters (62.7 inches) of precipitation every year, based on an average between 1981 and 2010.

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

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).

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.

Annual average precipitation represents the average total rainfall seen across the United States in each year. Data is sourced from the National Oceanic and Atmospheric Administration's National Center for Environmental information.

The Daily Climate Normals for 1991 to 2020 are 30-year averages of meteorological parameters that provide users the information needed to understand typical climate conditions for thousands of locations across the United States, as well as U.S. Territories and Commonwealths, and the Compact of Free Association nations. The stations used include 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 U.S. Climate Reference Network (USCRN) and other automated observation stations. In addition, precipitation normals for stations from the U.S. Snow Telemetry (SNOTEL) Network and the citizen-science Community Collaborative Rain, Hail and Snow (CoCoRaHS) Network are also available. The Daily Climate Normals dataset includes various derived products such as air temperature normals (including maximum and minimum temperature normals, heating and cooling degree day normals, and others), precipitation normals (including precipitation and snowfall totals, and percentiles, frequencies and other statistics of precipitation, snowfall, and snow depth), and agricultural normals (growing degree days (GDDs)). All data utilized in the computation of the 1991-2020 Climate Normals were taken from the Global Historical Climatology Network-Daily, but the Daily Normals are adjusted so that they are consistent with the Monthly Normals. The source datasets (including intermediate datasets used in the computation of products) are also archived at NOAA NCEI. A comparatively small number of station normals sets (~50) have been added as Version 1.0.1 to correct quality issues or because additional historical data during the 1991-2020 period has been ingested.

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

This metadata record describes the average daily precipitation intensity for rain events during the 30-year period 1981 – 2010 for the conterminous United States. A rain event is defined as a period when the number of consecutive days with precipitation equals or exceeds 1 millimeter. Daily precipitation intensity is defined as the amount of precipitation over the duration of a rain event divided by the number of days in a rain event. The source data was produced and acquired from DAYMET (2018) and is presented here as a 1-kilometer resolution GeoTIFF file.

This data release contains daily gridded data reflecting surface water input from rainfall, rain on snow (mixed), and snowmelt for the conterminous United States for water years 1990 to 2023 (1990/10/01 to 2023/09/30). This release also contains annual estimates of gridded input seasonality (an index reflecting whether surface water input occurs within a concentrated period or is equally distributed throughout the year), precipitation seasonality, average snowmelt, rainfall and rain on snow rates, and finally, annual totals of each input type. Average snowmelt, rainfall and rain on snow rates were computed using days where values were greater than zero. Daily data were generated using precipitation input from the gridMET dataset (Abatzoglou, 2013) and the University of Arizona snow water equivalent product (Broxton et al., 2019). Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131. ...

Monthly 30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1991-2020. Contains spatially gridded average annual total precipitation at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

These data represent mean annual precipitation (in inches) for Idaho for the climatological period 1961-90. Average annual precipitation is the average of the annual amount of precipitation for a location over a year. Data used to delineate these boundaries are from Idaho weather stations (1961-90).Source data for this web service can be downloaded from https://insideidaho.org/data/ago/ics/ppt_id_ics.zip.A printed map is available: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP71168920310001451 from the University of Idaho Map Room. Additionally, a related research report is available: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP71180601950001451.Related data set: Weather Stations Used to Compile the Mean Annual Precipitation Map for Idaho

This data layer is an element of the Oregon GIS Framework. Monthly 30-year "normal" dataset covering Oregon, averaged over the climatological period 1991-2020. Contains spatially gridded average annual total precipitation at 800m (30 arc-second) grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website.

Annual Climatological Summary contains historical monthly and annual summaries for over 8000 U.S. locations. Observing stations are located in the United States of America, U.S. Virgin Islands, Puerto Rico, and Pacific islands of the U.S. and associated nations. The major parameters are: monthly mean maximum, mean minimum and mean temperatures; monthly total precipitation and snowfall; departure from normal of the mean temperature and total precipitation; monthly heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; and extreme daily temperature and precipitation amounts. Annual Climatological Summary is derived from the NCDC Summary of the Month dataset (DSI-3220).

This is a Triangulated Irregular Network (TIN) of mean annual precipitation for the period 1931-80 for Ohio.

This map layer shows polygons of average annual precipitation in the contiguous United States, for the climatological period 1961-1990. Parameter-elevation Regressions on Independent Slopes Model (PRISM) derived raster data is the underlying data set from which the polygons and vectors were created. PRISM is an analytical model that uses point data and a digital elevation model (DEM) to generate gridded estimates of annual, monthly and event-based climatic parameters.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

This raster layer contains the average annual precipitation levels in inches for California from 1981 to 2010. This dataset incorporates a conceptual framework that uniquely addresses the spatial scale and pattern of orographic precipitation. The original PRISM dataset covered the United States. This is a California-only version subsetted from the original data set and converted to California Teale Albers NAD83 using bilinear interpolation by the California Department of Fish and Game (CDFG) at 800m resolution. The grid units are presented in inches with floating point. Care should be taken in estimating precipitation values at any single point on the map. Precipitation estimated for each grid cell is an average over the entire area of that cell; thus, point precipitation can be estimated at a spatial precision no better than half the resolution of a cell. Accuracy of this data set is based on the original specification of the Defense Mapping Agency (DMA) 1 degree digital elevation models (DEMs). The stated accuracy of the original DEMs is 130m circular error with 90% probability.