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.

Contained within the 4th Edition (1974) of the Atlas of Canada is a collection of six maps. Each map shows the average monthly precipitation for April, May, June, July, August and September.

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.

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 July 2025, the average precipitation amounted to 114 liters per square meter, a drastic increase compared to the previous month. The rainiest state in Germany was Saarland.

30-year Average precipitation represents the average amount (mm) of precipitation received in a month across a 30 year period (1961-1991, 1971-2000, 1981-2010, 1991-2020). These values are calculated across Canada in 10x10 km cells.

Datasets provide monthly precipitation data for 31 Iranian cities from 1901 to 2022. The data was obtained from the CRU-HRG dataset (https://crudata.uea.ac.uk/cru/data/hrg/) using specialized GIS workflows. The dataset consists of 1464 rows, with each row representing a month over 120 years, spanning from 1901 to 2022. Each column contains the average monthly rainfall for each city, measured in millimeters (mm).

This dataset serves as a valuable resource for researchers and decision-makers interested in understanding precipitation patterns in Iran. It can be used to analyze long-term precipitation trends, assess the impacts of climate change, and aid in natural resource management.

Please note:
This dataset contains satellite-derived climate data from the website https://crudata.uea.ac.uk. Satellite data are measured using sensors that may be subject to error. Therefore, it is possible that these data may differ from ground-based observations, which are typically used to generate real-world data. This difference is generally greater in remote areas and regions with high cloud.

In 2024, the United States saw some **** 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 **** inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering ***** inches (*** meters) of precipitation—nearly **** inches (ca. ** centimeters) above its historical average. In stark contrast, Nevada received only **** inches (ca. ** 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 ***** 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 *****.

Precipitation in Iran increased to 235.19 mm in 2024 from 199.18 mm in 2023. This dataset includes a chart with historical data for Iran Average Precipitation.

Precipitation in the United States increased to 777.25 mm in 2024 from 738.01 mm in 2023. This dataset includes a chart with historical data for the United States Average Precipitation.

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.

Australian Bureau of Meteorology assembled this dataset of 191 Australian rainfall stations for the purpose of climate change monitoring and assessment. These stations were selected because they are believed to be the highest quality and most reliable long-term rainfall stations in Australia. The longest period of record is August 1840 to December 1990, but the actual periods vary by individual station. Each data record in the dataset contains at least a monthly precipitation total, and most records also have daily data as well.

This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues:

LAPrec1871 starts in 1871 and is based on data from 85 input series; LAPrec1901 starts in 1901 and is based on data from 165 input series.

This allows user flexibility in terms of requirements defined by temporal extent or spatial accuracy. LAPrec was constructed to satisfy high climatological standards, such as temporal consistency and the realistic reproduction of spatial patterns in complex terrain. As the dataset covers over one-hundred years in temporal extent, it is a qualified basis for historical climate analysis in a mountain region that is highly affected by climate change. The production of LAPrec combines two data sources:

HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century.

APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges.

The adopted reconstruction method, Reduced Space Optimal Interpolation (RSOI), establishes a linear model between station and grid data, calibrated over the period when both are available. RSOI involves a Principal Component Analysis (PCA) of the high-resolution grid data, followed by an Optimal Interpolation (OI) using the long-term station data. The LAPrec dataset is updated on a two-year basis, by no later than the end of February each second year. The latest version of the dataset will extend until the end of the year before its release date. LAPrec has been developed in the framework of the Copernicus Climate Change Service in a collaboration between the national meteorological services of Switzerland (MeteoSwiss, Federal Office of Meteorology and Climatology) and Austria (ZAMG, Zentralanstalt für Meteorologie und Geodynamik). For more information on input data, methodical construction, applicability, versioning and data access, see the product user guide in the Documentation tab. The latest version of the dataset will temporally extend until the end of the year before its release date.

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.

This file contains average rainfall (mm) and average temperature (centigrade) for the North East England and East England for period 2010-2019.

This dataset shows the average rainfall in millimeters and average temperature in centigrade by month, year, and meteorological season. It also has an annual figure for each year.

Average Rainfall for the Months (June to September) from 2010 to 2019

Q: Was the month drier or wetter than usual? A: Colors show where and by how much monthly precipitation totals differed from average precipitation for the same month from 1991-2020. Green areas were wetter than the 30-year average for the month and brown areas were drier. White and very light areas had monthly precipitation totals close to the long-term average. Q: Where do these measurements come from? A: Daily measurements of rain and snow come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers or automated instruments gather the data and submit them to the National Centers for Environmental Information (NCEI). After scientists check the quality of the data to omit any systematic errors, they calculate each station’s monthly total and plot it on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolates (or estimates) values, accounting for the distribution of stations and various physical relationships, such as the way temperature changes with elevation. The resulting product is the NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid). To calculate the percent of average precipitation values shown on these maps—also called precipitation anomalies—NCEI scientists take the total precipitation in each 5x5 km grid box for a single month and year, and divide it by its 1991-2020 average for the same month. Multiplying that number by 100 yields a percent of average precipitation. If the result is greater than 100%, the region was wetter than average. Less than 100% means the region was drier than usual. Q: What do the colors mean? A: Shades of brown show places where total precipitation was below the long-term average for the month. Areas shown in shades of green had more liquid water from rain and/or snow than they averaged from 1991 to 2020. The darker the shade of brown or green, the larger the difference from the average precipitation. White and very light areas show where precipitation totals were the same as or very close to the long-term average. Note that snowfall totals are reported as the amount of liquid water they produce upon melting. Thus, a 10-inch snowfall that melts to produce one inch of liquid water would be counted as one inch of precipitation. Q: Why do these data matter? A: Comparing an area’s recent precipitation to its long-term average can tell how wet or how dry the area is compared to usual. Knowing if an area is much drier or much wetter than usual can encourage people to pay close attention to on-the-ground conditions that affect daily life and decisions. People check maps like this to judge crop progress; monitor reservoir levels; consider if lawns and landscaping need water; and to understand the possibilities of flooding. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products; to meet the needs of a broad audience, we present the source data in a simplified visual style. This set of snapshots is based on climate data (NClimGrid) produced by and available from the National Centers for Environmental Information (NCEI). To produce our images, we invoke a set of scripts that access the source data and represent them according to our selected color ramps on our base maps. Additional information The data used in these snapshots can be downloaded from different places and in different formats. We used these specific data sources: NClimGrid Total Precipitation NClimGrid Precipitation Normals References NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) NOAA Monthly U.S. Climate Divisional Database (NClimDiv) Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions NCEI Monthly National Analysis Climate at a Glance - Data Information NCEI Climate Monitoring - All ProductsSource: https://www.climate.gov/maps-data/

Monthly Rainfall and Temperatures (UK)

Met Office Climate District

By data.world's Admin [source]

About this dataset

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How to use the dataset

To use this dataset, start by making sure you are familiar with the following fields: OrganisationName, OrganisationCode, PublishedDate, DurationFrom (start date of reported period), DurationTo (end date of reported period), LatestData (indicating if latest available data is provided or not), GeoName (name of geographical area being reported on), ReportingPeriodType (type of reporting period i.e monthly/yearly/seasonal etc.), Year, Rainfallmm(average rainfall in millimeters), Temp(average temperature in centigrade), Dataset Name(name of the dataset provided). These are all important pieces of information that must be known before delving into the other columns.

Research Ideas

• Developing predictive models for drought and flooding with the help of average temperature and rainfall data
• Producing reports to inform farmers on various farming activities that need to be done depending on the climate conditions in the region
• Creating visualizations which can compare historical trends of average temperature and rainfall in different regions

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: average-rainfall-temperature-1.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------------| | OrganisationName | Name of the organisation providing the data. (String) | | OrganisationCode | Code associated with the name of the organisation providing the data. (String) | | PublishedDate | Date when that particular set of data was published. (Date) | | DurationFrom | Start date of that respective period. (Date) | | DurationTo | End date of the respective period. (Date) | | LatestData | It specifies whether or not that particular set is available to you. (Boolean) | | GeoName | Place/location where these climatic conditions exists. (String) | | ReportingPeriodType | Specifies whether it is a monthly/yearly report. (String) | | Year | Indicates year for which these statistical values have been obtained. (Integer) | | Rainfallmm | Average rainfall in millimetres during specified period. (Float) | | Temp | Average temperature in centigrade during specified period. (Float) |

File: average-rainfall-temperature-metatdata-2.csv | Column name | Description | |:--------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dataset Name | Name of the dataset. (String) | | Field | Details a certain aspect or parameter amongst numerous parameters present within a resultset. (String) | | Type | Whether its Numerical value or DoT notation. (String) | | Mandatory or Optional requirement (MOR) | This field tells us if we require anything specific while submitting our queries. (String) | | Field Description | A brief overvie...

A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

Gridded files of average monthly precipitation in the Nehterlands. Measured on +- 300 locations of the voluntary network. Based on 28 automatic weather stations.