## 1 Introduction  
Vegetation as a component of terrestrial biosphere, is a pivotal link between land surface and atmosphere [1, 2]. It exerts significant influence on water balance, via evapotranspiration [3, 4, 5], interception [6] and rooting strategy [7]. Vegetation also shows its ecological importance in regulating carbon cycle with processes of respiration and photosynthesis [8, 9, 10, 11]. Changes in vegetation coverage and biomass can lead to changes in water cycle and ecology balance. Studies have shown that vegetation growth has been strongly influenced by global climate change [12, 13]. Hence, understanding the influence of climate on vegetation dynamics is crucial for further evaluation of hydrology and ecology response, and meanwhile helps clarify the relative contribution of human activities to vegetation dynamics. The influence of climate on vegetation dynamics has drawn increasing attention in the past few decades [14, 15, 16, 17, 18, 19, 20]. Long-term remote sensing observations provide effective methods to investigate interannual vegetation dynamics. The Normalized Difference Vegetation Index (NDVI), which is correlated to Leaf Area Index (LAI) [21] and Gross Primary Production (GPP) [22], is a good indicator for vegetation coverage and biomass, and has been widely used to analyze vegetation dynamics [23, 24, 25, 26, 27]. Many researches have investigated NDVI trends in different regions, showing that despite great spatial heterogeneity, NDVI generally increased in national and regional scale in China [19, 28], North America [2], east Asia [29] and Euraisa [30]. However, the increasing trends in these areas have slowed down or reversed since mid-1990s, which could be attributed to slowed down temperature trends or more frequent and severe drought [29, 31]. The change in NDVI trends were analyzed by comparing trends before and after certain change points. Relationships between NDVI and climatic drivers were typically analyzed with regression method, indicating that NDVI was more sensitive to temperature than precipitation in North America and China and increased corresponding to global warming [2, 32]. Most previous studies mainly focused on mean NDVI values within a fixed time period, such as the whole year or uniform growing season (April to October) [20, 30, 33], which may not properly reflect vegetation growth condition in the growing season that varies with region and vegetation types [34, 35]. Especially for China, with its large land area and vegetation diversity, there can be significant difference among the growing seasons in different regions and vegetation types. For example, deciduous broad leaf vegetation in some areas of southern China can have high level of activity from April to late November or even December while grass in parts of Tibetan merely show high activity from late May to early October [28, 35]. Moreover, relationships between climatic drivers and NDVIwere considered individually with single regression model in previous studies [2, 19, 30, 32]. But since climatic drivers are affecting vegetation growth simultaneously, the contribution of each climate driver to the coupled effect on vegetation dynamics remains unclear. Most previous studies mainly focused on the linear trends and correlations between vegetation growth and climatic drivers. Nevertheless, apart from the linear relationships, non-linear responses such as abrupt change, when the trends or mean values significantly altered before and after the change point, may also exist. The abrupt change points provide information on how and when the responding patterns have changed, which cannot be revealed in the overall linear relationships, hence still deserve further understanding. The objective of this study is to investigate the interannual dynamics of natural vegetation and the influence of climatic drivers in China mainland during the past three decades, with the newest vegetation data and the ground observation based climate data with higher resolution than previous studies [19, 32, 36].