In June 2025, the average precipitation amounted to 61 liters per square meter, an increase compared to the previous month. The rainiest state in Germany was Saarland.

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.

The map shows the mean total precipitation in the month of July. Throughout much of the continental interior of Canada, precipitation reaches its annual maximum in the summer months and falls as rain. On the Prairies, the maximum monthly precipitation is usually in June or July, but this shifts to August at more northerly latitudes and in Ontario and Quebec. On both the west and east coasts, summer is the driest time of the year, particularly on Vancouver Island and the Sunshine Coast of southwestern British Columbia. In the Arctic Archipelago, rainfall tends to be dominant, but snowfall is still significant and can occur in any summer month.

This dataset provides global estimates of precipitation based on satellite observations. Precipitation is the main component of water transport from the atmosphere to the Earth’s surface within the hydrological cycle. It varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. The present dataset allows the investigation and quantification of these aspects of precipitation, possibly in conjunction with other datasets covering components of the hydrological cycle. The data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Spaceborne passive microwave (MW) imagers and sounders, available on different Low Earth Orbit (LEO) platforms, provide the most effective measurements for the remote sensing of precipitation because of the sensitivity of the MW upwelling radiation to the cloud microphysical properties, especially the emission and scattering of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of overpasses per day (depending on latitude and number of platforms) at each location. A further ECV Precipitation product only based on MW observations, COBRA, is also available in the CDS. On the other hand, infrared (IR) sensors onboard geostationary (GEO) platforms, provide only information on the upper-level cloud structure, but at much higher temporal and spatial resolution, for example improving the representative sampling of intermittent precipitation. Since precipitation is not directly observed in the infrared, these measurements are often merged with microwave-based precipitation estimates. This precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1° gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1° gridded monthly mean of daily accumulation. In the above sense, GIRAFE optimizes the sampling of precipitation by merging observations by LEO MW imagers and sounders (Level-2 data) with GEO-Ring IR brightness temperatures (Level-1 data). The daily accumulated precipitation is also aggregated to monthly mean precipitation. This dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.

The wettest months in the United Kingdom tend to be at the start and end of the year. In the period of consideration, the greatest measurement of rainfall was nearly 217 millimeters, recorded in December 2015. The lowest level of rainfall was recorded in April 2021, at 20.6 millimeters. Rainy days The British Isles are known for their wet weather, and in 2024 there were approximately 164 rain days in the United Kingdom. A rainday is when more than one millimeter of rain falls within a day. Over the past 30 years, the greatest number of rain days was recorded in the year 2000. In that year, the average annual rainfall in the UK amounted to 1,242.1 millimeters. Climate change According to the Met Office, climate change in the United Kingdom has resulted in the weather getting warmer and wetter. In 2022, the annual average temperature in the country reached a new record high, surpassing 10 degrees Celsius for the first time. This represented an increase of nearly two degrees Celsius when compared to the annual average temperature recorded in 1910. In a recent survey conducted amongst UK residents, almost 80 percent of respondents had concerns about climate change.

Using observation data from various agencies in Taiwan, including the Central Weather Bureau, Water Resources Agency, Irrigation Agency and Taiwan Power Company, supplementary, homogenization, and gridization operations were carried out to establish grid data with a resolution of 5 kilometers throughout Taiwan. This data was produced by the "Taiwan Climate Change Projection Information and Adaptation Knowledge Platform Project" of the National Science Council.

The Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group works on a range of projects, some of which support the development of spatial climate datasets. These PRISM datasets provide estimates of the basic climate element of precipitation (ppt), or the Daily total precipitation averaged over a month for both rain and melted snow. These datasets are modeled with PRISM using a digital elevation model (DEM) as the predictor grid and provide baselines describing average monthly precipitation between 1961 and 1990 to be used for display and/or analyses requiring spatially distributed monthly or annual precipitation.

Historical Past (1895-1980) - Time series datasets prior to 1981 are modeled using climatologically-aided interpolation (CAI), which uses the long-term average pattern (i.e., the 30-year normals) as first-guess of the spatial pattern of climatic conditions for a given month or day. CAI is robust to wide variations in station data density, which is necessary when modeling long time series. Data is based on Monthly and Annual dataset covering the conterminous U.S. from 1981 to now. Contains spatially gridded monthly and 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.

Daily rainfall is the total rainfall accumulated over the 24 hour period up until 9am on the day of observation. Monthly rainfall is the total rainfall accumulated over all days in the given month.

Of the various climate zones the world is divided into, tropical rainforest areas generally experience the highest levels of rainfall, while deserts experience the lowest. Of the examples given, Manaus in the Amazon rainforest experienced the highest average rainfall in 11 months of the year, with a range between 68mm in August to 310mm in April. However, the example with the largest relative variation between seasons is the shrubland, which ranged from an average of 4mm in July to 156mm in January. In Cairo, in Egypt's Western Desert, monthly rainfall in all summer months was less than one millimeter.

The amount of monthly rainfall in Northern Ireland varies from year to year. During the period in consideration, the lowest rainfall levels were recorded in April 2021 at just **** millimeters. Meanwhile, the most rainfall occurred in February 2020, when ***** millimeters fell. In that same month, there were **** rain days recorded, an unusually high number for February. A rain day is when there is a total of 1 mm or more of rain in a day. Seasonal rainfallSince 2010,********has been on average the wettest season in Northern Ireland. In 2023, however, ****** was the wettest season, with nearly *** mm of rainfall. That year, winter was the driest season, with *** mm of rainfall. Regional rainfallWhen compared to the rest of the UK, Northern Ireland receives less rain than both Scotland and Wales, but more than England. In 2024, the country experienced****** mm of rainfall. In comparison, Scotland and Wales received ***** and ******mm, respectively. This is due to the Scottish Highlands high levels of rain and Wales’ location in comparison to the Atlantic Ocean.

Since 2015, the greatest monthly rainfall deviation in the United Kingdom occurred in February 2020. This month saw a considerable increase of 139 millimeters from the long-term mean. In comparison, the same month in 2023 saw a decrease of almost 40 millimeters compared to the mean from 2002 to 2021.

Total monthly precipitation modeled globally by NASA . The map shows monthly precipitation for the period of 2000 to the present, focused on the Caribbean.Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt. Dataset SummaryThe GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available.This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

This dataset has monthly 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 .

What does the data show?

The data shows monthly averages of rainfall amount (mm) for 1991-2020 from HadUK gridded data. It is provided on a 2km British National Grid (BNG).

What are the naming conventions and how do I explore the data?

This data contains a field for each month’s average over the period. They are named 'pr' (precipitation) and the month. E.g. 'pr March' is the average rainfall amount for March in the period 1991-2020.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr January’ values

Data source:

HadUK-Grid v1.1.0.0 (downloaded 11/03/2022)

Useful links

Further information on HadUK-Grid Further information on understanding climate data within the Met Office Climate Data Portal

This dataset contains Bahrain Monthly Values of Relative Humidity, Temperature, Rainfall, Sunshine Hours, Thunder Storm, Dust Storm, Fog and Wind Speed for Data from Bahrain Open Data Portal. Follow datasource.kapsarc.org for timely data to advance energy economics research.Rainfall: 0.05 values is originally recorded as Trace which is = < 0.05 Millimeters > zero.Storms/Fog measure: Number of days.

These data are transitioned to a state of permanent preservation. They are available upon request. More advanced datasets have been developed since. One recommended replacement is the GPCP (doi: 10.5067/DBVUO4KQHXTK) product developed under the MEaSUREs project. Futhermore, the NASA Precipitation Measurement Missions Project released newly processed SSM/I datasets as output from the GPROF (Goddard Profiling Algorithm). (doi: 10.5067/GPM/SSMI/F11/GPROFCLIM/2A/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-MONTH/05, 10.5067/GPM/SSMI/F11/GPROFCLIM/3A-DAY/05) The "RAIN_CHANG" SSM/I Derived Oceanic Monthly Rainfall Indices data set was an early Global Precipitation Climatology Project (GPCP) product. Monthly rainfall indices over the oceans were derived from Special Sensor Microwave Imager (SSM/I) data from the Defense Meteorological Satellite Program (DMSP) satellites F8 and F11, on channels 19 and 22 V. The data set covered the period from July 1987 to December 1995. The monthly rainfall indices are on a 5 degree by 5 degree grid extending from 50 N to 50 S. The Wilheit, Chang and Chiu (1991) method used to derive the indices gives valid values only over ocean areas. Land pixels (including island pixels) and erroneous pixels return a -10 flag. The data are stored on a 72 x 20 grid. Grid point (1,1) contains the index for 45-50 N, 0-5 E, grid point (2,1) contains the index for 45-50 N, 5-10 E, ... and grid point (72,20) contains the index for 45-50 S, 175-180 W. In the data set, each month starts with an ASCII header to identify the year and month. The data is in 10F8.1 format. Each value is the average of AM and PM estimates and corrected for beam filling error. The equation used is: (AM PM)/2.0 * 1.8. Land pixels are set to -10.0. Also there are 33 pixels blocked out due to island contamination (-10.0). If the rain retrieval did not converge, a -10.0 is assigned to the pixel. The objective of this data set was to provide a long term monthly rainfall data set to be used in EOS global change and GEWEX related research.

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

The average rainfall chart shows the average amount of total rainfall, or amount of all liquid precipitation in millimetres (mm) such as rain, drizzle, freezing rain, and hail, observed at the location for each month of the specified year. Precipitation is measured using vertical depth of water (or water equivalent in the case of solid forms) which reaches the ground during a stated period.

This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites. Spaceborne passive microwave (MW) provides the most effective measurements for the remote sensing of precipitation because the MW upwelling radiation is directly responsive to the cloud microphysical structure and, in particular, to the emission and scattering properties of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of passes per day (depending on latitude and number of platforms) at each location. On the other hand, infrared (IR) sensors, available also on geostationary platforms, provide measurements that mostly respond to upper-level cloud structure, but at much higher temporal and spatial resolution. Since precipitation is not directly sensed in the infrared, these observations are often merged with microwave-based precipitation estimates and rain gauges. A precipitation product merging IR and MW is also available on the Climate Data Store: GPCP precipitation dataset. The two different radiometer classes used in the present Copernicus micrOwave-based gloBal pRecipitAtion (COBRA) dataset are: i) Conically scanning MW imagers; observations obtained by applying methodologies of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) in the Satellite Application Facility on Climate Monitoring (CM SAF). ii) Cross-track scanning MW sounders; observations obtained through the dedicated Passive microwave Neural network Precipitation Retrieval for Climate Applications (PNPR-CLIM) algorithm. This datset is independent of IR imagery and rain-gauge observations. A pure passive MW-based precipitation dataset overcomes the challenges and limitations of precipitation estimates based on IR observations, and the issues related to the inadequacy of the rain gauge networks in some regions and their almost complete absence over the ocean. The main limitations, however, are linked to the varying (in time and space) revisiting time of the LEO satellites and low temporal sampling compared to geostanionary IR observations. This dataset is produced by the Copernicus Climate Change Service (C3S).