The interactive patterns of elements in terrestrial vegetation-atmosphere exchange are complex， some are even poorly understood. Linear or general linear methods have been widely used in exploring vegetation dynamic and climatic changes. Yet linear thinking may inhibit our understanding of complex nonlinear systems and the unravelling causality behind linear correlation is difficult to extract directly from observational data. Here， we aimed to quantify the vegetation-climate interactions， using nonlinear dynamical methods based on state-space reconstruction and datasets from Chinese meteorological station and remote sensing data during 1982~2015， in Northeast China （NEC）. Specifically， we detected the causal links between meteorological factors （temperature， precipitation） and vegetation index （NDVI） by reconstructing the state space from historical records. During the study period， vegetation has a strong bi-directional causal relationship with temperature and precipitation across Northeast China. The value of NDVI can be well reconstructed from the state information of meteorological factors （temperature， precipitation）. The strength of the interactions varied across different vegetation types with various meteorological factors， in which coniferous forests， broadleaf forests， and shrublands are more influenced by temperature than causal effects on temperature. The intensity of the driving effect of temperature on vegetation gradually increases from north to south， and the low intensity zones mainly occur in the coniferous forest area in the northern part. The slight effect of precipitation-vegetation cross-mapping skills are found in the north-eastern mountains， eastern plains and mountainous areas. Our results suggest that the balance between positive and negative effects of precipitation on vegetation is influenced by temperature. When temperatures greater than 0℃， the effect of precipitation on vegetation changes from negative to positive. In contrast， the effect of temperature on vegetation was weaker compared to precipitation， but when the precipitation was greater than 800 mm， the increase in temperature showed a roughly negative upward trend on vegetation. Exploring the causality between vegetation and meteorological factors in Northeast China can improve the understanding of climate change and vegetation feedback at mid and high-latitude regions. Our work also suggests that nonlinear exploration may have the potential to discovering new knowledges in earth science.