February 23, 2026 Dataset Open Access

Yakubu, Fuseini; Böhner, Jürgen; Bouwer, Laurens M.; ul Hasson, Shabeh

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{"@context":"https://schema.org/","@id":"http://doi.org/10.25592/uhhfdm.18335","@type":"Dataset","citation":[{"@id":"http://doi.org/10.5281/zenodo.4686991","@type":"CreativeWork"},{"@id":"http://doi.org/10.5194/gmd-12-3055-2019","@type":"CreativeWork"},{"@id":"http://doi.org/10.48364/ISIMIP.342217","@type":"CreativeWork"}],"contributor":[{"@type":"Person","affiliation":"Integrated Climate Data Center (ICDC), Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany","name":"Sadikni, Remon"},{"@id":"https://orcid.org/0000-0001-7281-3746","@type":"Person","affiliation":"Integrated Climate Data Center (ICDC), Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany","name":"Kern, Stefan"}],"creator":[{"@id":"https://orcid.org/0009-0009-4653-9945","@type":"Person","affiliation":"1.\tHAREME Lab, Institute of Geography, Earth and Society Research Hub, Universit\u00e4t Hamburg, Hamburg, Germany, and 3.\tClimate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany","name":"Yakubu, Fuseini"},{"@id":"https://orcid.org/0000-0002-0842-0152","@type":"Person","affiliation":"2.\tInstitute of Geography, Earth and Society Research Hub, Universit\u00e4t Hamburg, Hamburg, Germany","name":"B\u00f6hner, J\u00fcrgen"},{"@type":"Person","affiliation":"2.\tInstitute of Geography, Earth and Society Research Hub, Universit\u00e4t Hamburg, Hamburg, Germany, and 3.\tClimate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany","name":"Bouwer, Laurens M."},{"@id":"https://orcid.org/0000-0002-7791-9635","@type":"Person","affiliation":"1.\tHAREME Lab, Institute of Geography, Earth and Society Research Hub, Universit\u00e4t Hamburg, Hamburg, Germany","name":"ul Hasson, Shabeh"}],"datePublished":"2026-02-23","description":"<p><strong>Abstract:&nbsp;</strong>The BC-HiRMIP dataset provides globally consist, comprehensive bias-corrected climate datat of High Resolution Model Intercomparison Project (HighResMIP) experiments at daily temporal and 0.5&deg; spatial resolution, covering the period 1979-2050 for hist-1950 and highres-future scenario. Across four global climate models (MPI-ESM1-2-XR, EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC31-HM), BC-HiRMIP includes up to 11 essential meteorological variables (temperature suite, precipitation, humidity, radiation, wind, and pressure), spanning equilibrium climate sensitivities of 2.99-5.62&deg;C. For more information see &quot;BC-HiRMIP.pdf&quot; and &quot;Data Description-BC-HiRMIP.pdf&quot;</p>\n\n<p>The data are available in netCDF file format per model, run (historical or future), and variable.</p>\n\n<p><strong>TableOfContents:</strong> daily mean near-surface air temperature (tas); daily minimum near-surface air temperature (tasmin), daily maximum near-surface air temperature (tasmax); daily sum of precipitation (pr); daily sum of precipitation in form of snow (prsn)*; daily mean surface downwelling shortwave radiation (rsds). daily mean surface downwelling longwave radiation (rlds); daily mean near-surface wind speed (sfcWind); daily mean near-surface relative humidity (hurs); daily mean near-surface specific humidity (huss)**; daily mean surface air pressure (ps)**</p>\n\n<p>*) Only&nbsp;CNRM-CM6-1-HR and&nbsp;MPI-ESM1-2-XR</p>\n\n<p>**) Only&nbsp;EC-Earth3P-HR and HadGEM3-GC31-HM</p>\n\n<p><strong>TechnicalInfo:</strong> dimension: 720 columns x 360 rows; temporalExtent_startDate_hist-1950: 1979-01-01 00:00:00; temporalExtent_endDate_hist-1950: 2014-12-31 23:59:59; temporalDuration_hist-1950: 36; temporalDurationUnit_hist-1950: a; temporalExtent_startDate_highres-future: 2015-01-01 00:00:00; temporalExtent_endDate_highres-future: 2050-12-31 23:59:59; temporalDuration_highres-future: 36; temporalDurationUnit_highres-future: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 0.5; spatialResolutionUnit: degree; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degree; horizontalResolutionYdirection: 0.25; horizontalResolutionYdirectionUnit: degree; verticalResolution: none; verticalResolutionUnit: none</p>\n\n<p><strong>Methods:&nbsp;</strong> The ISIMIP3BASD v2.5 bias correction method (see Lange [<a href=\"http://doi.org/10.5194/gmd-12-3055-2019\">2019</a>; <a href=\"http://doi.org/10.5281/zenodo.4686991\">2021</a>]) was applied to adjust systematic biases while preserving the climate change signals. This parametric quantile mapping approach:<br>\n&bull; Corrects biases across all percentiles of variable distributions<br>\n&bull; Preserves trends in these percentiles<br>\n&bull; Applies variable-specific treatments (e.g., handling drizzle issues for precipitation)<br>\n&bull; Maintains physical relationships between variables (particularly for temperature variables)</p>\n\n<p>using the W5E5 V2.0 (<a href=\"https://doi.org/10.48364/ISIMIP.342217\">Lange et al., 2021)</a> observational reference dataset at 0.5 degree resolution. The four climate models participating in the HighResMIP experiment that are used here are:&nbsp;</p>\n\n<ul>\n\t<li>MPI-ESM1-2-XR</li>\n\t<li>EC-Earth3P-HR</li>\n\t<li>CNRM-CM6-1-HR</li>\n\t<li>HadGEM3-GC31-HM</li>\n</ul>\n\n<p>The historical runs &quot;hist-1950&quot; begin 1979-01-01 and end 2014-12-31. The future projection runs &quot;highres-future&quot; begin 2015-01-01, end 2050-12-31, and follow the ssp5-8.5 scenario pathway.&nbsp;The dataset was remapped from their native grids resolution to match the reference dataset grid using CDO: conservative remapping for flux variables (precipitation and radiation) and bilinear remapping for other continuous variables.</p>\n\n<p>The routines (python) used to create and work with the data sets are available from this web page as well: Bias_CorrectonScript.pr and DeriveHUSS.py</p>\n\n<p><strong>Quality:</strong>&nbsp;The bias-correction method used is of high quality and has been used for similar datasets multiple times.&nbsp;The performance of the bias correction in replicating the same distribution as that of the W5E5 v2.0 dataset was evaluated for diverse areas located in five different climate types after K&ouml;ppen-Geiger with convincing results, illustrating the skill of the bias-correction method especially with respect to the distribution of values across the expected range of variation (see also the document BC-HiRMIP.pdf)</p>\n\n<p><strong>Units:</strong> K; K; K; kg m-2 s-1; kg m-2 s-1; W m-2; W m-2; m s-1; percent; kg kg-1; pa</p>\n\n<p><strong>GeoLocation:</strong> westBoundCoordinate: -180.0; westBoundCoordinateUnit: degrees East; eastBoundCoordinate: 180.0; eastBoundCoordinateUnit: degrees East; southBoundCoordinate: -90.0; southBoundCoordinateUnit: degrees North; northBoundCoordinate: 90.0; northBoundCoordinateUnit: degrees North</p>\n\n<p><strong>Size:</strong>&nbsp;CNRM-CM6-1-HR: 203.9 GByte, MPI-ESM1-2-XR: 204.2 GByte, EC-Earth3P-HR: 249.9 GByte, HadGCM3-GC31-HM: 250.1 GByte</p>\n\n<p><strong>Format:</strong> netCDF</p>\n\n<p><strong>DataSources:</strong> See the file &quot;Data Description-BC-HiRMIP.pdf&quot;</p>\n\n<p><strong>Contact:</strong> fuseini.yakubu (at) uni-hamburg.de; shabeh.hasson (at) uni-hamburg.de</p>\n\n<p><strong>Webpage:</strong> <a href=\"https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html\">https://www.geo.uni-hamburg.de/geographie/abteilungen/physische-geographie/arbeitsgruppen/ag-hareme.html</a></p>","distribution":[{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/f9981ce8-7977-403a-b371-a962260d4ba4/BC-HiRMIP.pdf","encodingFormat":"pdf"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/f9981ce8-7977-403a-b371-a962260d4ba4/Bias_CorrectionScript.py","encodingFormat":"py"},{"@type":"DataDownload","contentUrl":"https://www.fdr.uni-hamburg.de/api/files/f9981ce8-7977-403a-b371-a962260d4ba4/Data 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