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The spatial and temporal variability of ANPP in Tibetan Plateau (TP) grasslands and its response to temperature and precipitation were investigated in this study, based on the ANPP estimated by means of ensemble analysis of multiple machine learning models. First, the response of the spatial distribution of ANPP to variations in the precipitation gradient was nonlinear and showed an S-shaped growth pattern, which could be divided into four stages (stationary, rapid growth, slower growth, and saturation) corresponding to arid (mean annual precipitation (MAP) &lt; 250 mm), semiarid (250 mm &lt; MAP &lt; 450 mm), semihumid (450 mm &lt; MAP &lt; 650 mm) and humid (MAP &gt; 650 mm) precipitation regimes, respectively. Second, regional precipitation regimes affected the sensitivity of ANPP\u2019s interannual variability to temperature and precipitation; ANPP is more sensitive to temperature in wetter areas than in dryer areas of the TP region. The results of our study suggest that in the region of Tibetan Plateau, the response of grassland ANPP variation to climate was constrained by the background precipitation regimes.<\/jats:p>","DOI":"10.3390\/rs15102591","type":"journal-article","created":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T01:58:06Z","timestamp":1684288686000},"page":"2591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Precipitation Conditions Constrain the Sensitivity of Aboveground Net Primary Productivity in Tibetan Plateau Grasslands to Climate Change"],"prefix":"10.3390","volume":"15","author":[{"given":"Na","family":"Zeng","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Environment and Resources, Zhejiang A & F University, Hangzhou 311300, China"},{"name":"National Ecology Science Data Center, Beijing 100101, China"}]},{"given":"Xiaoli","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"National Ecology Science Data Center, Beijing 100101, China"}]},{"given":"Honglin","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"National Ecology Science Data Center, Beijing 100101, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0423-5494","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"National Ecology Science Data Center, Beijing 100101, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhongen","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Ludong University, Yantai 264025, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.5194\/essd-11-1783-2019","article-title":"Global Carbon Budget 2019","volume":"11","author":"Friedlingstein","year":"2019","journal-title":"Earth Syst. 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