The metadata record summarizes information for time series of monthly weather measurements from the 18 meteo stations of AGE. The dataset goes back 25 years in time, starting at the foundation of each station. The kind of measurements differ from station to station.

This dataset is available via the WMS Time (https://wms.inspire.geoportail.lu/geoserver/mf/wms?service=WMS&version=1.3.0&request=GetCapabilities) and WFS (https://wms.inspire.geoportail.lu/geoserver/mf/wfs?service=WFS&version=2.0.0&request=GetCapabilities) API protocols. See for example the following sample requests:

WFS: https://wms.inspire.geoportail.lu/geoserver/mf/wfs?SERVICE=wfs&VERSION=2.0.0&REQUEST=GetFeature&TRANSPARENT=true&TYPENAME=MF.PointTimeSeriesObservation_Monthly_AGE_sum_nn050&srsName=EPSG:3857&OUTPUTFORMAT=application/json&CQL_FILTER=datetime%20BEFORE%202020-01-01T00:00:00Z%20%20AND%20datetime%20AFTER%202018-12-31T00:00:00Z%20AND%20name_descr=%20%27Bigonville%27

WMS Time: https://wms.inspire.geoportail.lu/geoserver/mf/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&FORMAT=image%2Fpng&TRANSPARENT=true&STYLES&LAYERS=mf%3AMF.PointTimeSeriesObservation_Monthly_AGE_sum_nn050&CRS=EPSG%3A3035&WIDTH=474&HEIGHT=769&BBOX=2914640.6691353433%2C4006619.263203916%2C3031916.5131074684%2C4079000.448155462&TIME=2020-05-01T00:00:00.000Z

Data is transformed into INSPIRE data model.

Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean te...

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

The metadata record summarizes information for time series of monthly weather measurements from the 41 meteo stations of ASTA. The dataset goes back a maximum of 25 years in time, starting at the foundation of each station. The kind of measurements differ from station to station. This dataset is available via the WMS Time (https://wms.inspire.geoportail.lu/geoserver/mf/wms?service=WMS&version=1.3.0&request=GetCapabilities) and WFS (https://wms.inspire.geoportail.lu/geoserver/mf/wfs?service=WFS&version=2.0.0&request=GetCapabilities) API protocols. See for example the following sample requests: WFS: https://wms.inspire.geoportail.lu/geoserver/mf/wfs?SERVICE=wfs&VERSION=2.0.0&REQUEST=GetFeature&TRANSPARENT=true&TYPENAME=MF.PointTimeSeriesObservation_Monthly_ASTA_avg_ta200&srsName=EPSG:3857&OUTPUTFORMAT=application/json&CQL_FILTER=datetime%20BEFORE%202020-01-01T00:00:00Z%20%20AND%20datetime%20AFTER%202018-12-31T00:00:00Z%20AND%20name_descr=%20%27Arsdorf%27 WMS Time: https://wms.inspire.geoportail.lu/geoserver/mf/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&FORMAT=image%2Fpng&TRANSPARENT=true&STYLES&LAYERS=mf%3AMF.PointTimeSeriesObservation_Monthly_ASTA_avg_ta200&CRS=EPSG%3A3035&WIDTH=474&HEIGHT=769&BBOX=2914640.6691353433%2C4006619.263203916%2C3031916.5131074684%2C4079000.448155462&TIME=2020-05-01T00:00:00.000Z Data is transformed into INSPIRE data model. Description copied from catalog.inspire.geoportail.lu.

Monthly mean gridded land temperature and total precipitation on a 1/2 degree grid from 1900 to 2014 (V4). Sources are from the GHCN2 (Global Historical Climate Network) and, more extensively, from the archive of Legates & Willmott.

The Russian weather stations included in this analysis are Krenkel Observatory, Nagurskaya, Rudolf Island and Ostrov Victoria. The temperature data from the stations have undergone both manual and automatic quality controls in several stages. The data were initially manually controlled at the weather station by the observers and have later undergone several rounds of manual and automatic quality control including consistency checks and outlier tests. Tests to identify large errors and suspicious observations in the temperature series included logical tests using differences between maximum, minimum and mean temperature. To identify outliers, Grubbs’ criterion was used where values exceeding ±2.5 standard deviation from the monthly mean were marked and examined. A modified Tietjen-Moore test, was sometimes used to test outliers. All suspicious values were examined by experts at AARI (Arctic and Antarctic Research Institute), RIHMI-WDC (All-Russia Research Institute of Hydrometeorological Information - World Data Center) or SPSU (Saint Petersburg State University) who made the final decision on whether to keep or reject the value. The temperature series were also compared to series from neighboring stations to identify possible systematic errors giving shifts in the data series. The homogenized temperature series from Krenkel Observatory also includes data from the weather station Bukhta Thikaya and has been carefully scrutinized as described by Ivanov et al. 2021*.*Svalbard Airport, Ny-Ålesund and Hopen are weather stations intended for forecasting and climate analysis and the data from these stations undergo extensive quality control (QC) when being stored in MET Norway’s database. Quality control has been performed mostly manually until 2005 when an automatic QC routine was put into use that includes several consistency tests such as step tests and threshold tests, in addition to manual inspection of values flagged as suspicious by the system. There have been several changes in instrumentation and location at all three stations leading to breaks in the homogeneity of the series. More details on quality control, station changes, and homogeneity can be found in Førland et al. 2011, Nordli et al. 2015 & 2020, Gjelten et al. 2016, and Hanssen-Bauer et al. 2019. During a time span of nearly thirty years automatic weather stations (AWS) have been in operation on the northern and eastern islands of Svalbard. The instruments and station infrastructure have varied much during those years. During the early years the data were not stored in MET Norway’s database, and there was no quality control. There were also problems with the regularity of the data, in particular many stations were destroyed by polar bears. In 1996, no data of accepted quality reached MET Norway. However, in 2010 a new setup of stations was developed, which improved data quality and significantly reduced the number of missing data. Hence, almost all our work on data control for this study was related to data before the autumn of 2010.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview-National Overview summarizes observations of surface temperature and precipitation data for the United States by placing the data into historical perspective. The National Overview provides access to monthly, 3-month/seasonal, 6-month, 12-month and annual climate summaries by state, division and region. Topics include: Surface Temperature (rankings maps and time series), Precipitation (rankings maps and time series), Percent Area wet/dry cold/warm, and Primary Crop Region Maps with Season-to-Date Precipitation Time series. Additional information on current seasonal summaries for snow, fire, severe weather, and Atlantic and Pacific hurricanes, is also provided. The first annual report was presented in 1998, and monthly reports are available thereafter. Summary reports are often included with the final month of the period, for example, the spring report is issued along with May. "Year-to-date" reports are also often available for months that don't end a meteorological season.

The Weather Generator Gridded Data consists of two products:

[1] statistically perturbed gridded 100-year historic daily weather data including precipitation [in mm], and detrended maximum and minimum temperature in degrees Celsius, and

[2] stochastically generated and statistically perturbed gridded 1000-year daily weather data including precipitation [in mm], maximum temperature [in degrees Celsius], and minimum temperature in degrees Celsius.

The base climate of this dataset is a combination of historically observed gridded data including Livneh Unsplit 1915-2018 (Pierce et. al. 2021), Livneh 1915-2015 (Livneh et. al. 2013) and PRISM 2016-2018 (PRISM Climate Group, 2014). Daily precipitation is from Livneh Unsplit 1915-2018, daily temperature is from Livneh 2013 spanning 1915-2015 and was extended to 2018 with daily 4km PRISM that was rescaled to the Livneh grid resolution (1/16 deg). The Livneh temperature was bias corrected by month to the corresponding monthly PRISM climate over the same period. Baseline temperature was then detrended by month over the entire time series based on the average monthly temperature from 1991-2020. Statistical perturbations and stochastic generation of the time series were performed by the Weather Generator (Najibi et al. 2024a and Najibi et al. 2024b).

The repository consists of 30 climate perturbation scenarios that range from -25 to +25 % change in mean precipitation, and from 0 to +5 degrees Celsius change in mean temperature. Changes in thermodynamics represent scaling of precipitation during extreme events by a scaling factor per degree Celsius increase in mean temperature and consists primarily of 7%/degree-Celsius with 14%/degree-Celsius as sensitivity perturbations. Further insight for thermodynamic scaling can be found in full report linked below or in Najibi et al. 2024a and Najibi et al. 2024b.

The data presented here was created by the Weather Generator which was developed by Dr. Scott Steinschneider and Dr. Nasser Najibi (Cornell University). If a separate weather generator product is desired apart from this gridded climate dataset, the weather generator code can be adopted to suit the specific needs of the user. The weather generator code and supporting information can be found here: https://github.com/nassernajibi/WGEN-v2.0/tree/main. The full report for the model and performance can be found here: https://water.ca.gov/-/media/DWR-Website/Web-Pages/Programs/All-Programs/Climate-Change-Program/Resources-for-Water-Managers/Files/WGENCalifornia_Final_Report_final_20230808.pdf

This is the first of two datasets containing derived data necessary to reproduce the results of the associated journal article: "On the Sensitivity of Annual Streamflow to Air Temperature." This first dataset contains basic basin characteristics of 2,673 gaged basins worldwide, along with associated monthly time series of basin-mean precipitation, air temperature, and net radiation. The streamflow data themselves are available directly from the Global Runoff Data Centre. From the inventory of discharge data holdings of the Global Runoff Data Centre, 2,673 stream gages were selected for which (1) at least 25 complete calendar years of monthly data overlapped in time with available climate data; (2) 500-m-resolution basin shapefiles were available from Global Runoff Data Centre; (3) the Global Reservoir and Dam database listed no artificial reservoirs located within the drainage basin; and (4) mean reported discharge did not exceed either of two estimates of basin precipitation by more than a factor of 10. Monthly precipitation data from the Global Precipitation Climatology Centre, both precipitation and temperature from University of East Anglia Climatic Research Unit, and net radiation data from NASA, all on 0.5-degree grids, were areally averaged over the basins.

The gridded CRU TS (time-series) 3.00 data are month-by-month variations in climate over the period 1901-2006, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

The CRU TS 3.00 variables include cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum/min temperature, vapour pressure and wet day frequency.

The BADC holds the preliminary CRU TS3.00 datasets for the period 1901-2006. Note that these data are yearly files REGARDLESS of whether the data exists for the full year. In CRU TS3.00, the data up to 06/2006 ONLY has been used.

The CRU TS 3.00 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.

All CRU TS output files are actual values - NOT anomalies.

CRU TS data are available for download to all CEDA users.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each month going back to 1880. Be sure to configure the time settings in your web map to view the time series correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. A version showing just the most recent month is available here.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey (USGS) have evaluated projections of future droughts for south Florida based on climate model output from the Multivariate Adaptive Constructed Analogs (MACA) downscaled climate dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The MACA dataset includes both Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5). A Microsoft Excel workbook is provided which tabulates monthly climate-anomaly timeseries for the period January 1950 through December 2099. Anomaly timeseries are provided for precipitation, reference evapotranspiration (ETo), and balances (precipitation - ETo) averaged over regions of interest in every MACA model. The anomaly timeseries are computed as the departure from the long-term monthly means for the period 1950-2005. Climate data are derived from climate models downscaled by the MACA method assuming the Kruijt stomatal resistance (rs) curve in the future for four regions: (1) the entire South Florida Water Management District (SFWMD), (2) the Lower West Coast (LWC) water supply region, (3) the Lower East Coast (LEC) water supply region, and (4) the Okeechobee plus (OKEE+) water supply meta-region consisting of Lake Okeechobee (OKEE), the Lower Kissimmee (LKISS), Upper Kissimmee (UKISS), and Upper East Coast (UEC) water supply regions in the SFWMD.

Based on the Multiple Analysis of Series for Homogenization (MASH) and CLIMATOL methods, we firstly detected, estimated, and adjusted inhomogeneities in daily mean temperature, maximum temperature, minimum temperature and precipitation at 366 stations in China from 1960 to 2021, then established homogenized daily temperature and precipitation data sets. All datasets are completed by cooperation between Greece (Laboratory of Atmospheric Physics, Department of Physics, University of Patras) and China (Key Laboratory of Regional Climate-Environment in Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences).The information are as following:Homogenized monthly and daily temperature and precipitation time series in China during 1960-2021.rar (118MB). There is a .xlsx file named Information at 366 stations in China, including four columns (No. Station No. Longitude, and Latitude) Four folders are named RR, TG, TN, and TX. In each folder there are two subfolders named daily and monthly. In each subfolder, there are 366 .csv files named with station No. In each .csv file, there are 2 columns (date and records). In updated version, the climate change indices dataset is deleted, because I will submit it again to SDB as an another dataset. In this version, the monthly and daily temperature and precipitation time series dataset is updated to 2021.

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

Q: How warm do afternoons usually get during this month? A: Based on daily observations from 1991-2020, colors on the map show the long-term average maximum temperature, sometimes referred to as the daytime or afternoon high, in 5x5 km grid cells for the month displayed. The map reveals the average of daytime high temperatures during the month over the previous three decades. Q: Where do these measurements come from? A: Daily temperature readings come from weather stations in the Global Historical Climatology Network (GHCN-D). Volunteer observers and automated instruments collected the highest temperature at each station every day from 1991 to 2020, and sent them to the National Centers for Environmental Information (NCEI). After scientists checked the quality of the data to omit any systematic errors, they calculated each station’s average monthly maximum temperature by taking the sum of all the daily maximum temperatures for a month (for example, all Junes in the 1991-2020 period) and dividing it by the total number of daily measurements (the number of days in the month times 30 years). NCEI scientists then plotted the values on a 5x5 km gridded map. To fill in the grid at locations without stations, a computer program interpolated (or estimated) 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). Q: What do the colors mean? A: The color in each 5x5 km grid cell shows the average of the highest temperature recorded every day of the month for the 30 years from 1991 to 2020. Shades of blue show where the highest daily temperatures measured from 1991 to 2020 averaged below 50°F for the month. The darker the shade of blue, the lower the temperature. Areas shown in shades of orange and red have long-term average maximum temperatures above 50°F. The darker the shade of orange or red, the higher the temperature. White or very light colors show areas where the average maximum temperature is near 50°F. Q: Why do these data matter? A: Understanding these values provides insight into the “normal” conditions for a month. This type of information is widely used across an array of planning activities, from designing energy distribution networks, to the timing of crop and plant emergence, to choosing the right place and time for recreational activities. 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 NClimGrid climate data 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 this specific data source: NClimGrid Temperature 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 Products Source: https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-maximum-1991-2020-monthly-a...This upload includes two additional files:* Temperature - Maximum, 1991-2020 Monthly Average _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/temperature-maximum-1991-2020-monthly-a

This dataset includes global mean surface temperature anomalies for each month from 1850 to 2024. The impacts of sea ice concentrations and an internal variability pattern on surface temperature anomalies are also available. Estimated temperature anomalies include these impacts. The internal variability pattern corresponds well to the El Niño Southern Oscillation. Results of sensitivity tests using alternate sea ice source datasets from the Japanese Meteorological Agency (COBE-SST3) and the National Snow and Ice Data Center (modified G10010v2 appended with G02202v4) are also available.

The maximum likelihood estimation approach allows for the estimated global mean surface temperature anomalies to be temporally complete for the entire time period. Other estimates of this dataset are also temporally complete.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The data updates monthly, usually around the 15th of the following month. For instance, the January data will become available on or about February 15th. The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report that summarizes the data is released each month (and end of the year) by NOAA NCEI is available here. GHCN monthly mean averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here. What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each month going back to 1880. Be sure to configure the time settings in your web map to view the time series correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. A version showing just the most recent month is available here.

Measurements of surface air and ocean temperature are compiled from around the world each month by NOAA’s National Centers for Environmental Information and are analyzed and compared to the 1971-2000 average temperature for each location. The resulting temperature anomaly (or difference from the average) is shown in this feature service, which includes an archive going back to 1880. The mean of the 12 months each year is displayed here. Each annual update is available around the 15th of the following January (e.g., 2020 is available Jan 15th, 2021). The NOAAGlobalTemp dataset is the official U.S. long-term record of global temperature data and is often used to show trends in temperature change around the world. It combines thousands of land-based station measurements from the Global Historical Climatology Network (GHCN) along with surface ocean temperature from the Extended Reconstructed Sea Surface Temperature (ERSST) analysis. These two datasets are merged into a 5-degree resolution product. A report summary report by NOAA NCEI is available here. GHCN monthly mean station averages for temperature and precipitation for the 1981-2010 period are also available in Living Atlas here.What can you do with this layer? Visualization: This layer can be used to plot areas where temperature was higher or lower than the historical average for each year since 1880. Be sure to configure the time settings in your web map to view the timeseries correctly. Analysis: This layer can be used as an input to a variety of geoprocessing tools, such as Space Time Cubes and other trend analyses. For a more detailed temporal analysis, a monthly mean is available here.

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.03 data are month-by-month variations in climate over the period 1901-2018, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.

The CRU TS4.03 variables are cloud cover, diurnal temperature range, frost day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2018.

The CRU TS4.03 data were produced using angular-distance weighting (ADW) interpolation. All version 4 releases used triangulation routines in IDL. Please see the release notes for full details of this version update.

The CRU TS4.03 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.

All CRU TS output files are actual values - NOT anomalies.

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.