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.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

This dataset measures extreme rainfall at 30 sites across Aotearoa New Zealand from 1960 to 2022. We measure the maximum amount of rainfall in a single day (‘maximum precipitationl’), the number of very wet days (‘very wet days’), and the percentage of annual rainfall from very wet days (‘very wet day precipitation percent’). We present trends against the 1961 to 1990 climate normal period as well as the 1991 to 2020 climate normal period for very wet days and the percentage of annual rainfall from very wet days.

Variables: site: NIWA climate site reference_period: Reference period against which the number of wet days was calculated parameter: maximum precipitation (mm), very wet days, very wet day precipitation percent (%) period_start: Start of trend period period_end: End of trend period p_value: P value slope: Sen’s slope statistic of rate of change conf_low: Confidence intervals for Sen’s slope statistic conf_high: Confidence intervals for Sen’s slope statistic conf_level: Confidence level (90% or 66%) for Sen’s slope statistic z: z score trend_method: Mann-Kendall or Sen’s slope method n: Number of data points included in trend calculation note: note on data point s: Mann-Kendall test statistics var_s: Mann-Kendall test statistics tau: Mann-Kendall test statistics alternative: Alternative hypothesis trend_likelihood: Likelihood of trend direction adapted from IPCC criteria lat: Latitude lon: Longitude site_simple: site without macrons

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.

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 *****.

Annual rainfall trends for 30 representative sites from 1960–2016. Rain is vital for life – it supplies the water we need to drink and to grow our food, keeps our ecosystems healthy, and supplies our electricity. New Zealand’s mountainous terrain and location in the roaring forties mean rainfall varies across the country. Changes in rainfall amount or timing can significantly affect agriculture, energy, recreation, and the environment. For example, an increase or decrease of rainfall in spring can have marked effects on crops or fish populations. Trend direction was assessed using the Theil-Sen estimator and the Two One-Sided Test (TOST) for equivalence at the 95% confidence level. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

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.

Information on the spatio-temporal distribution of rainfall is very critical for addressing water related disasters, especially in the arid to semi-arid regions of the Middle East and North Africa region. However, availability of reliable rainfall datasets for the region is limited. In this study we combined observation from satellite-based rainfall data, in situ rain gauge observation and rainfall climatology to create a reliable regional rainfall dataset for Jordan, West Bank and Lebanon. First, we validated three satellite-based rainfall products using rain gauge observations obtained from Jordan (205 stations), Palestine (44 stations) and Lebanon (8 stations). We used the daily 25-km Tropical Rainfall Measuring Mission over 2000 – 2016; daily 10-km Rainfall Estimate for Africa (RFE) rainfall over 2001 – 2016; daily 5-km Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall over 1981-2015; daily 25-km Multi-Source Weighted-Ensemble Precipitation (MSWEP) over 1984-2015. The validation was conducted between in situ rain gauge observation and satellite rainfall data and resulted in utilizing the MSWEP dataset in correlation with a bias correction grid. The created rainfall dataset was used to estimate stream flow in the region and determine suitable areas of aquifer recharge.

This study addresses rainfall trends, the frequency of droughts, La Niña influences and the relationship between rainfall and Sea Surface Temperature (SST) in Tuvalu. The findings revealed that;

• de-trended rainfall time series show declining trends in all four rainfall stations over the period 1953-2012;

• the frequency of drought ranges from three to fourteen years with a mean of nine years

• the occurrence of drought appears to follow the La Niña years

• boplots provide an effective option for defining drought

• there is empirical support for a moderate to strong correlation between the de-trended values of SST and rainfall in the area of study

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.

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, 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.

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.

The Boston Water and Sewer Commission (BWSC) maintains collection sites throughout the city. Those collection sites are equipped with solar powered rain gauges on top of public buildings which log measurements of precipitation and which report data every five minutes. Here you find the link to the Boston Water and Sewer Commission’s interface to the rainfall data, which is updated continually. You can search for rainfall data going as far back as 1999, depending on the year of installation for the various gauges.

CHIRPS is an abbreviation for Climate Hazards Group InfraRed Precipitation with Station Data (Version 2.0 final). The CHIRPS is a 30+ year quasi-global rainfall dataset and incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. The data of the CHIRPS pentad is derived from Google Earth Engine with earth engine snippet as https://code.earthengine.google.com/?scriptPath=Examples%3ADatasets%2FUCSB-CHG_CHIRPS_PENTAD . With the dataset in a global format, it is clipped with the Cambodia boundary and generated the data visualized chart through the obtained data.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are 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.

This is GLO rainfall data including the period 1970 to 2102 in netCDF format. This dataset was used as climate forcing to drive surface and ground water modelling.

The seasonal scaling factors associated with CSIRO-Mk3.0 that are +4.5%, -2.1%, -4.5% and -2.8% for summer, autumn, winter and spring are used to generate trended climate inputs for the years 2013 to 2102. The trends assume global warming of 1-degree for the period 2013 to 2042, compared to 1983 to 2012. The global warming for 2043 to 2072 is assumed to be 1.5 degrees and the corresponding scaling factors for this period are therefore multiplied by 1.5. The global warming for 2073 to 2102 is assumed to be 2 degrees.

Dataset History

The future rainfall data were obtained using the historical rainfall multiplied by seasonal scaling factors.

The seasonal scaling factors associated with CSIRO-Mk3.0 that are +4.5%, -2.1%, -4.5% and -2.8% for summer, autumn, winter and spring are used to generate trended climate inputs for the years 2013 to 2102. The trends assume global warming of 1-degree for the period 2013 to 2042, compared to 1983 to 2012. The global warming for 2043 to 2072 is assumed to be 1.5 degrees and the corresponding scaling factors for this period are therefore multiplied by 1.5. The global warming for 2073 to 2102 is assumed to be 2 degrees.

The scaling factors are applied to scale the daily precipitation in the climate input series that is generated for 2013 to 2102 (Zhang et al., 2016).

Reference:

Zhang YQ, Viney NR, Peeters LJM, Wang B, Yang A, Li LT, McVicar TR, Marvanek SP, Rachakonda PK, Shi XG, Pagendam DE and Singh RM (2016) Surface water numerical modelling for the Gloucester subregion. Product 2.6.1 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment and Energy, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.1.

Dataset Citation

Bioregional Assessment Programme (2016) GLO Future climate rainfall v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/82b95cec-50cb-4811-94f9-23e75cb3d9ce.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

The AdjDlyRS dataset contains adjusted daily rainfall (R) and snowfall (S) data from all Canadian stations reporting rainfall and snowfall for which we have metadata to do the adjustments (Wang et al. 2017). The processing includes inspection and adjustments using quality control procedures customized for producing gridded datasets (Wang et al. 2017), including: (1) conversion of snowfall ruler measurements to their water equivalents; (2) corrections for gauge undercatch and evaporation due to wind effect, for gauge specific wetting loss, and for trace precipitation amount; and (3) treatment of flags (e.g. accumulation flags). Version 2020 or later versions of this dataset also includes identification and correction of random erroneous values, including false zeros, which usually arose from missing values being misrecorded as 0 precipitation in the climate Archive (Cheng et al. 2022). All the identified erroneous daily values are set to missing. A total of 3346 stations were processed, but the data series are not homogenized. Most of the stations are located in southern Canada and have short and/or seasonal data records. The number of stations changes over time: there are 512-958 stations in the period 1948-1964, 1012-2038 stations in the period 1965-2008, and only around 300 stations in the recent years. Note that the unadjusted/raw total precipitation data in Environment and Climate Change Canada's digital Archive underestimate more than 25% of the total precipitation in northern Canada, and about 10-15% in most of southern Canada (Wang et al. 2017). References: (1) Wang, X. L., Xu, B. Qian, Y. Feng, E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada, Atmosphere-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. (2) Cheng, V. Y.S., X. L. Wang, Y. Feng, 2022: A quality control system for historical in situ precipitation data. Atmosphere-Ocean (submitted)

The California Department of Water Resources (DWR), Northern Region Office (NRO), maintains 33 precipitation stations that were installed starting in 1944. Stations record total annual precipitation. This information can help inform annual water budgets or track climate-related trends in annual precipitation. The CSV file contains total annual precipitation data in inches. The PDF file contains a description of stations and methods for data collection.

This dataset measures annual and seasonal rainfall trends at 30 sites across Aotearoa New Zealand from 1960 to 2022.

Variables site: NIWA climate site. season: Season or Annual data (combined for ease of data use) trend_likelihood: Likelihood of trend direction adapted from IPCC criteria. period_start: Start of trend period period_end: End of trend period p_value: P value slope, conf_low, conf_high, conf_level: Slope statistic to describe rate of change and relevant 90% and 66% confidence intervals. intercept: Intercept r_sqared: R squared sigma: Sigma trend_method: Trend method (Mann-Kendall or Linear model) n: Number of data points included in trend calculation. note: Linear model analysis notes s, var_s, tau, z: Mann-Kendall test statistics. alternative: Alternative hypothesis used in Mann Kendall Calculation lat: Approximate latitude location of NIWA climate stations to represent a site. lon: Approximate longitude location of NIWA climate stations to represent a site. simple_site: site without macrons

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.