In 2024, the United States saw some 31.6 inches of precipitation. The main forms of precipitation include hail, drizzle, rain, sleet, and snow. Since the turn of the century, 2012 was the driest year on record with an annual precipitation of 27.5 inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering 71.25 inches (1.8 meters) of precipitation—nearly 14.4 inches (ca. 37 centimeters) above its historical average. In stark contrast, Nevada received only 9.53 inches (ca. 24 centimeters), underscoring the vast differences in rainfall across the nation. These extremes illustrate the uneven distribution of precipitation, with the southwestern states experiencing increasingly dry conditions that experts predict will worsen in the coming years. Drought concerns persist Drought remains a significant concern in many parts of the country. The Palmer Drought Severity Index (PDSI) for the contiguous United States stood at -3.39 in December 2024, indicating moderate to severe drought conditions. This reading follows three years of generally negative PDSI values, with the most extreme drought recorded in December 2023 at -3.93.

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.

[Metadata] Mean Annual Rainfall Isohyets in Millimeters for the Islands of Hawai‘i, Kaho‘olawe, Kaua‘i, Lāna‘i, Maui, Moloka‘i and O‘ahu. Source: 2011 Rainfall Atlas of Hawaii, https://rainfall.geography.hawaii.edu/. Note that Moloka‘I data/maps were updated in 2014. Please see Rainfall Atlas final report appendix for full method details: https://rainfall.geography.hawaii.edu/downloads.html. Statewide GIS program staff downloaded data from UH Geography Department, Rainfall Atlas of Hawaii, February, 2019. Annual and monthly isohyets of mean rainfall were available for download. The statewide GIS program makes available only the annual layer. Both the monthly layers and the original annual layer are available from the Rainfall Atlas of Hawaii website, referenced above. For additional information, please see metadata at https://files.hawaii.gov/dbedt/op/gis/data/isohyets.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

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

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two maps for the annual total precipitation. Annual precipitation is defined as the sum of rainfall and the assumed water equivalent of snowfall for a given year. A specific gravity of 0.1 for freshly fallen snow is used, which means that ten inches (25.4 cm) of freshly fallen snow is assumed to be equal to one inch (2.54 cm) of rain. The mean annual total precipitation and snowfall maps on this plate are primarily based on thirty-year data during the period 1921 to 1950 inclusive.

Mexico recorded some 590 millimeters of precipitation in 2023. This represented a decrease of over 20 percent in comparison to the previous year and the lowest figure reported since the turn of the century. Meanwhile, the Latin American country registered its wettest year in 2010, with over 962 millimiters of rainfall.

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.

This map is part of a series of global climate images produced by the Agrometeorology Group and based on data for mean monthly values of temperature, precipitation and cloudiness prepared in 1991 by R. Leemans and W. Cramer and published by the International Institute for Applied Systems Analysis (IIASA). For each of the weather stations used data have been assembled over a long time period - usually between 1961 and 1990 - and then averaged. Annual totals for rainfall were derived from the monthly values.

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).

U.S. 15 Minute Precipitation Data is digital data set DSI-3260, archived at the National Climatic Data Center (NCDC). This is precipitation data. The primary source of data for this file is approximately 2,000 mostly U.S. weather stations operated or managed by the U.S. National Weather Service. Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precip occurs) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. NCDC has in archive data from most states as far back as 1970 or 1971, and continuing to the present day. The major parameter is precipitation amounts at 15 minute intervals, when precipitation actually occurs.

"Annual rainfall is the total accumulated rain over one year. Rain is vital for life, including plant growth, drinking water, river ecosystem health, and sanitation. Floods and droughts affect our environment, economy, and recreational opportunities.

This dataset shows annual average rainfall across New Zealand for 1993 as part of the data series for years 1972 to 2013. Annual rainfall is estimated from the daily rainfall estimates of the Virtual Climate Station Network (NIWA).

This dataset relates to the "Annual average rainfall" measure on the Environmental Indicators, Te taiao Aotearoa website.

Geometry: grid Unit: mm/yr"

The Rainfall data consists of daily time series of rainfall data in millimeters. The amount of rainfall is measured using a rain gauge. A rain gauge consists of a cylindrical vessel assembly kept in the open to collect rain. Rainfall collected in the rain gauge is measured at regular intervals such as at around 08:00am in the City. Our Depots collect these readings every day and send the data to Head Office. read more

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.

PRISM is an analytical model that uses point data and an underlying grid such as a digital elevation model (DEM) or a 30 yr climatological average (e.g. 1971- 2000 average) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). 800m spacing. In the Smokies, the average annual rainfall varies from approximately 55 inches in the valleys to over 85 inches on some peaks-more than anywhere else in the country except the Pacific Northwest, qualifying these upper elevation areas as temperate rain forests. During wet years, over eight feet of rain falls in the high country. The relative humidity in the park during the growing season is about twice that of the Rocky Mountain region. The broad range of elevations in the Great Smoky Mountains (<1000 to 6642 ft asl) contributes to the wide variety of climates therein. At the lower elevations (ca. 1000 ft) the climate is humid mesothermal with precipitation distributed throughout the year. At the uppermost elevations, which are among the highest attained in the Appalachian chain, the relatively cool, wet climate is perhumid microthermal. It supports evergreen coniferous forest vegetation rather than the deciduous forest vegetation typical of lower elevations. Total annual precipitation in the high-elevation coniferous forests rivals that of some of the wettest regions of the United States.

Annual rainfall seasonality is an index derived from two ratios. The ratio of warm (Oct-Nov-Dec-Jan-Feb-Mar) to cool (Apr-May-Jun-Jul-Aug-Sep) season log-rainfall totals (minus 1) are assigned positive values when rainfall in the warm season is greater than rainfall in the cool season. The ratio of cool to warm season log-rainfall totals (plus 1) are assigned negative values when rainfall in the cool season is greater than rainfall in the warm season.

Ivory Coast Maximum 5-day Rainfall: 25-year Return Level data was reported at 9.549 mm in 2050. Ivory Coast Maximum 5-day Rainfall: 25-year Return Level data is updated yearly, averaging 9.549 mm from Dec 2050 (Median) to 2050, with 1 observations. The data reached an all-time high of 9.549 mm in 2050 and a record low of 9.549 mm in 2050. Ivory Coast Maximum 5-day Rainfall: 25-year Return Level data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Environmental: Climate Risk. A 25-year return level of the 5-day cumulative precipitation is the maximum precipitation sum over any 5-day period that can be expected once in an average 25-year period.;World Bank, Climate Change Knowledge Portal (https://climateknowledgeportal.worldbank.org);;

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 StatesPRECIPITATIONEach climate model estimates an amount of precipitation (whether rain, snow, sleet, or ice) that occurs every 3 hours across the entire modeled time period (i.e., every 3 hours, of every day, for all modeled years). These 3-hour precipitation estimates can be used to calculate the total precipitation over a designated period of time, ranging from daily to annually. Argonne calculated total annual precipitation by adding all 3-hour precipitation estimates for a given 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), which produced the total annual precipitation for that scenario's model year, such as CCSM's estimate of annual precipitation in 2045 under climate scenario RCP4.5. This process was repeated for each year within a given time period/scenario (e.g., 2046, 2047, and so forth) and across all three climate models (CCSM, GFDL, and HadGEM), producing a total of 30 estimates of total annual precipitation for a given time period/scenario. The average of these values was taken to produce the ensemble mean of the total annual precipitation (in inches) for each time period/scenario. CONSECUTIVE DAYS WITH NO PRECIPITATIONEach climate model estimates an amount of precipitation (whether rain, snow, sleet, or ice) that occurs every 3 hours across the entire modeled time period (i.e., every 3 hours, of every day, for all modeled years). These 3-hour precipitation estimates were used to calculate daily precipitation quantities by adding all 8 precipitation readings for each day of a given 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). This process produced the total daily precipitation for every day in a scenario's model year, such as CCSM's daily estimates of total precipitation for the year 2045 under climate scenario RCP4.5. Using this information, Argonne identified the greatest number of consecutive days in which no precipitation occurred (i.e., the total daily precipitation quantity equaled zero) for that scenario's model year (e.g., for the year 2045 under scenario RCP4.5, the highest number of consecutive days without any precipitation was X). This process was repeated for each year within a given time period/scenario (e.g., 2046, 2047, and so forth) and across all three climate models (CCSM, GFDL, and HadGEM) producing 10 yearly values for each model, with each value representing the longest consecutive span with no precipitation for that year. Of the 10 yearly values for each climate model, the maximum value was selected (e.g., the decadal maximum). This resulted in 3 values for the longest consecutive number of days without precipitation for each time period/scenario, with one value for each climate model’s 10 years of data. The average of these maximum of the maxima was then taken to produce the ensemble mean of the decade’s highest number of consecutive days without precipitation in a single year. 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

Daily precipitation data were converted from tenths of mm to inches. Precipitation data were evaluated for outliers; data were excluded if values were -9999, no data, or less than 0.00 inches of rain. Recorded precipitation ranged from 0.0 inches on multiple days to 18.29 inches on August 29, 1911 at the Saint George, GA weather station. Median precipitation was 0.0 inches.

Abstract This dataset was derived by the Bioregional Assessment Programme from a source dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement The data contains mean annual rainfall for whole of Namoi subregion from 1900 to 2012. The last 30 years of data (1983 to 2012) is used to project rainfall data for three 30-year blocks starting from …Show full descriptionAbstract This dataset was derived by the Bioregional Assessment Programme from a source dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement The data contains mean annual rainfall for whole of Namoi subregion from 1900 to 2012. The last 30 years of data (1983 to 2012) is used to project rainfall data for three 30-year blocks starting from 2013 to 2102 using rainfall scaling factors. Purpose The data is used in the AWRA-L/R model to project future runoff under baseline and coal resource development pathway. The data is also used to plot time series of observed (1900 to 2012) and projected (2013 to 2102) annual precipitation averaged over the Namoi subregion using the script climate.m that takes input extends to three 30-year periods with scaling factors. bsmooth.m returns weighted smoothing of vector [x] over a window of length lwin = 23 years. The resulting plot is shown in NAM261 report. Dataset History This resource was created using BAWAP data. This source data was is the Bureau of Meteorology rainfall grids. The annual data were generated from daily data. Dataset Citation Bioregional Assessment Programme (2017) Namoi mean annual rainfall time series prediction. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/74aeaba9-1c47-443e-8940-2befe8ff8dce. Dataset Ancestors Derived From Mean climate variables for all subregions Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

Average rainfall in Spain amounted to some 536.6 millimeters in 2023. During the period in consideration, Spain's wettest year was 2018, when the average precipitation reached a record high of 808 millimeters. Since then, rainfall in the Mediterranean country has seen a continual annual decline.