The average for 2020 based on 178 countries was 1168 mm per year. The highest value was in Colombia: 3240 mm per year and the lowest value was in Egypt: 18 mm per year. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

In 2023, precipitation worldwide stood at **** inches below the annual average recorded across the previous century (1901 to 2000). In the past half-century, 2023 was the driest year on record. In contrast, 2010 was the wettest of the indicated period, with almost *** inches of rainfall above the annual average.

Typical annual rainfall data were summarized from monthly precipitation data and provided in millimeters (mm). The monthly climate data for global land areas were generated from a large network of weather stations by the WorldClim project. Precipitation and temperature data were collected from the weather stations and aggregated across a target temporal range of 1970-2000.

Weather station data (between 9,000 and 60,000 stations) were interpolated using thin-plate splines with covariates including elevation, distance to the coast, and MODIS-derived minimum and maximum land surface temperature. Spatial interpolation was first done in 23 regions of varying size depending on station density, instead of the common approach to use a single model for the entire world. The satellite imagery data were most useful in areas with low station density. The interpolation technique allowed WorldClim to produce high spatial resolution (approximately 1 km2) raster data sets.

This dataset provides values for PRECIPITATION AMOUNT OF PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2024, Louisiana recorded ***** 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 **** 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 **** 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.

Democratic Republic of the Congo: Precipitation, mm per year: The latest value from 2021 is 1543 mm per year, unchanged from 1543 mm per year in 2020. In comparison, the world average is 1168 mm per year, based on data from 178 countries. Historically, the average for Democratic Republic of the Congo from 1961 to 2021 is 1543 mm per year. The minimum value, 1543 mm per year, was reached in 1961 while the maximum of 1543 mm per year was recorded in 1961.

This dataset provides values for PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Russia: Precipitation, mm per year: The latest value from 2021 is 460 mm per year, unchanged from 460 mm per year in 2020. In comparison, the world average is 1168 mm per year, based on data from 178 countries. Historically, the average for Russia from 1992 to 2021 is 460 mm per year. The minimum value, 460 mm per year, was reached in 1992 while the maximum of 460 mm per year was recorded in 1992.

The purpose of this tool is to estimate daily precipitation patterns for a yearly cycle at any location on the globe. The user input is simply the latitude and longitude of the selected location. There is an embedded Zip Code search routine to find the latitude and longitude for US cities. GlobalRainSIM forecasts the daily rainfall based upon two databases.The first was the average number of days in a month with precipitation (wet days) that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995). The second database was the global average monthly precipitation data collected 1961-1990 and cross-validated by New et al. (1999). These two datasets were then used to establish the monthly precipitation totals and the frequency of precipitation in a month. The average precipitation event was calculated as the monthly mean divided by the number of wet days. This mean value was then randomly assigned to a day of the month looping through the number of wet days. In other words, if the average monthly rainfall was 10 mm/month with 5 average wet days, each rain event was 2 mm. This amount (2 mm) was then randomly assigned to 5 days of that month. The advantage of this tool is that a typical pattern of precipitation can be simulated for any global location arriving at an •average year• as a baseline case for comparison. This tool also outputs the daily rainfall as a file or can be easily embedded within another program. Resources in this dataset:Resource Title: Global RainSIM Verson 1.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=227&modecode=50-60-05-00 download page

Nigeria NG: Average Precipitation in Depth data was reported at 1,150.000 mm/Year in 2014. This stayed constant from the previous number of 1,150.000 mm/Year for 2012. Nigeria NG: Average Precipitation in Depth data is updated yearly, averaging 1,150.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 1,150.000 mm/Year in 2014 and a record low of 1,150.000 mm/Year in 2014. Nigeria NG: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

A global land-based gridded dataset of daily precipitation at 1 degree X 1 degree resolution from 1950 to 2013. Two datasets are available under the REGEN moniker. This dataset interpolates all daily precipitation stations available regardless of completeness of station timeseries.

A second related dataset interpolates only the long-term stations (stations with at least 40 complete years of data). Besides the grid cell average precipitation amount per day (mm/day), the Yamamoto standard deviation per grid cell (mm/day), the kriging error per grid cell (%) and number of stations per grid cell variables are also included.

Currently, there are two major data archives of global in situ daily rainfall data: 1. The Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and 2. The Deutscher Wetterdienst (DWD) Global Precipitation Climatology Centre (GPCC). These two data archives are combined along with additional station data acquired from other researchers. The merged archive is quality controlled and the flagged stations are removed to create a long term high quality archive of raw station data, which is then interpolated using ordinary block kriging by this dataset. The REGEN long-term dataset instead only interpolates a long-term station subset of this high quality merged archive.

The output consists of a separate CF-compliant netcdf file for each year, each containing the four aforementioned variables. These values are available for global land areas with the exception of Antarctica. The time dimension of the netcdf ranges from 1950-01-01 to 2013-12-31. Each dataset (All station based and long-term station based) is in classic netcdf format and occupies around 350MB of disk space for each year with the combined total of all years being around 26GB. Besides these two netcdfs, two additional netcdfs containing a mask indicating the high data quality grid cells of each dataset (all station and long-term) are also available. These netcdf files have the same grid descriptions as the original data but contain only one timestep for the entire period.

This dataset was produced by Steefan Contractor of the ARC Centre of Excellence for Climate Systems Sciences, as part of a the research program "The role of land surface forcing and feedbacks for regional climate".

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Average annual precipitation has seen an upward trend across most Nordic countries in the past century. The only exception was Denmark. Iceland and Norway have the highest average precipitation in the region, each with more than 1,100 millimeters recorded in 2022.

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).

In 2023, the annual average rainfall in Japan amounted to around **** thousand millimeters. Figures increased compared to about **** thousand millimeters in the previous year. Most of the rain fell during the rainy season, which is the time of year when most of a region's average annual rainfall occurs. Seasonal rainfall In most of Japan, the rainy season lasts from early June to mid-July. In the southernmost prefecture Okinawa, it roughly starts a month earlier, while the northernmost main island Hokkaido is less affected. Heavy rainfall can cause floods, which can lead to landslides and mudflows in mountainous areas. In recent years, flooded houses accounted for the highest number of damage situations in natural disasters. Furthermore, heavy rain and floods are often caused by typhoons, which develop over the Pacific Ocean and regularly approach the archipelago between July and October. Since the number of typhoons has increased in recent years, the amount of damage caused by floods grew as well. Climate change Climate change has affected Japan in recent years, resulting in increased rainfall and an increase of the average annual temperature in Tokyo. These weather changes can intensify natural disasters such as heavy rain and typhoons. In recent years, Japan was among the countries with the most natural disasters. To counter global warming, Japan aims to reduce greenhouse gas emissions by increasing its renewable and nuclear energy share.

This dataset has 1-day (daily) averages of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which is quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create a gridded rainfall time series for trend analysis and seasonal drought monitoring. Since 1999, USGS and CHC scientists (supported by funding from USAID, NASA, and NOAA) have developed techniques for producing rainfall maps, especially in areas where surface data is sparse. Estimating rainfall variations in space and time is a key aspect of drought early warning and environmental monitoring. See https://www.nature.com/articles/sdata201566 . See the FAQ at https://wiki.chc.ucsb.edu/CHIRPS_FAQ .

This dataset provides values for PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Belize BZ: Average Precipitation in Depth data was reported at 1,705.000 mm/Year in 2020. This stayed constant from the previous number of 1,705.000 mm/Year for 2019. Belize BZ: Average Precipitation in Depth data is updated yearly, averaging 1,705.000 mm/Year from Dec 1961 (Median) to 2020, with 60 observations. The data reached an all-time high of 1,705.000 mm/Year in 2020 and a record low of 1,705.000 mm/Year in 2020. Belize BZ: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belize – Table BZ.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.;Food and Agriculture Organization, electronic files and web site.;;

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA. Data available is the daily sum of precipitation. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Iran: Precipitation, mm per year: The latest value from 2021 is 228 mm per year, unchanged from 228 mm per year in 2020. In comparison, the world average is 1168 mm per year, based on data from 178 countries. Historically, the average for Iran from 1961 to 2021 is 228 mm per year. The minimum value, 228 mm per year, was reached in 1961 while the maximum of 228 mm per year was recorded in 1961.