作者单位：a State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, Chinab College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, Chinac CGCEO/Geography, Michigan State University, East Lansing, MI 48824, USAd Laboratoire d’études en géophysique et océanographie spatiales, LEGOS/CNES/CN RS/IRD/UPS-UMR5566, Toulouse, Francee GeoBiosphere Science Centre, Lund University, Sölvegatan 12, 223 62 Lund, Swedenf A.N.Severtsov Institute of Ecology and Evolution RAS 123103, Leninsky pr.33, Moscow, Russiag CNR-IBIMET, National Research Council, Via Caproni 8, 50145 Firenze, Italyh Dept. Forestry and Environmental Resources, North Carolina State University, 920 Main Campus Drive, Ste 300, Raleigh, NC 27695, USAi CESAM & Departamento de Ambiente e Ordenamento, Universidade de Aveiro, 3810-193 Aveiro, Portugalj Institute for Ecology, University of Innsbruck, Sternwartestr. 15, A-6020 Innsbruck, Austriak European Academy Bolzano, Drususallee 1, 39100 Bolzano, Italyl Eastern Forest Environmental Threat Assessment Center, Southern Research Station, United States Department of Agriculture Forest Services, Raleigh, NC 27606, USAm Bordeaux Sciences Agro, UMR INRA-ISPA 1391, 33195 Gradignan, Francen Nicholas School of the Environment, Duke University, Durham, North Carolina 27708, USAo Faculty of Science and Technology, Free University of Bolzano, Piazza Universita 5, 39100 Bolzano, Italyp Forest Services, Autonomous Province of Bolzano, 39100 Bolzano, Italyq Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmarkr Climate Change and Adaptive Land and Water Management, Alterra Wageningen UR, PO Box 47, 6700 AA Wageningen, The Netherlandss VU University Amsterdam, Boelelaan 1085, 1081HV Amsterdam, The Netherlandst Department of Geography, University of Colorado at Boulder, 260 UCB, Boulder, CO 80309-0260, USAu Technische Universität Dresden, Institute of Hydrology and Meteorology, Chair of Meteorology, D-01062 Dresden, Germanyv ETH Zurich, Institute of Agricultural Sciences, Universitaetsstr. 2, 8092 Zurich, Switzerlandw CNR-ISAFOM, National Research Council, Via Patacca 85, 80040 Ercolano (Napoli), Italy
•The model CESM1-CAM5 had the best performance with the highest predictive skill.•The BMA method has improved the accuracy of the CMIP5 GCM’s LE simulation.•A large difference from previous studies is an increasing LE trend after 1998.