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We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970\u20132000, using data from between 9000 and 60\u2009000 weather stations. Weather station data were interpolated using thin\u2010plate splines with covariates including elevation, distance to the coast and three satellite\u2010derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the\n                    <jats:styled-content style=\"fixed-case\">MODIS<\/jats:styled-content>\n                    satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5\u201315% (0.07\u20130.17\u00a0\u00b0C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross\u2010validation correlations were\u2009\u2265\u20090.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high\u2010quality network of climate station data.\n                  <\/jats:p>","DOI":"10.1002\/joc.5086","type":"journal-article","created":{"date-parts":[[2017,5,15]],"date-time":"2017-05-15T19:36:44Z","timestamp":1494877004000},"page":"4302-4315","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14652,"title":["WorldClim 2: new 1\u2010km spatial resolution climate surfaces for global land areas"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3548-6966","authenticated-orcid":false,"given":"Stephen E.","family":"Fick","sequence":"first","affiliation":[{"name":"Department of Plant Sciences University of California  Davis CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"Hijmans","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Policy University of California  Davis CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,5,15]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.614"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.1688"},{"key":"e_1_2_6_4_1","unstructured":"FAO.2001.FAOCLIM 2.0 A World\u2010Wide Agroclimatic Database.Food and Agriculture Organization of the United Nations:Rome Italy."},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1029\/2005RG000183"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0309133310375654"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2014.890304"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-11-00228.1"},{"key":"e_1_2_6_9_1","volume-title":"Google Earth Pro","author":"Google Inc.","year":"2013"},{"key":"e_1_2_6_10_1","unstructured":"Google Inc.2015. 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