## 1 Introduction  
Carbon dioxide (CO\({}_{2}\)) in the atmosphere has increased from 277 ppm in 1750 to 405 ppm in 2017 [1], which has mainly been ascribed to the combustion of fossil fuels, deforestation, and changes in land use. The terrestrial ecosystem is an important carbon sink and source, storing large amounts of carbon in plant parts and the soil [2]. Accordingly, the accurate assessment of the carbon distribution among terrestrial vegetation is important for properly understanding the global carbon budget, supporting climate policies, and undertaking project vegetation management in the future. Net primary productivity (NPP) is one of the components of the carbon cycle and represents vegetation biomass increases after accounting for autotrophic respiration [3; 4; 5; 6].  
The ecological process models, which rely on ground data, including climatology, meteorology, vegetation conditions, and soil conditions, are crucial methods for depicting vegetation activities (e.g., carbon, nitrogen, and water cycles) [7; 8; 9]. The manifestations of ecological processes are complex and diverse, including the physiological and ecological mechanisms of the plants themselves, the community succession of the ecosystem, and the numerous vegetation disturbances in the ecosystem. The boreal ecosystem productivity simulator (BEPS) is a process mechanism model that is driven by remote sensing data, and has been widely used in simulating vegetation productivity and evapotranspiration [10; 11; 12].  
Through the endeavors of many scholars, the original BEPS has experienced numerical developments. A \"bucket\" module was replaced by a three-layer soil module (0.10, 0.25, and 0.80 cm depth) [13]. Due to the simplified soil moisture mode that ignores the horizontal exchange of water, further processes, such as surface runoff, are added to the water circulation to improve the simulation accuracy of vegetation productivity [14]. After a series of model developments, BEPS is universally applied for depicting many important ecological processes and the interactions between these processes and the surroundings.  
Northeast China is covered by rich vegetation types (forest, grass, cropland) and is one of the regions that are most sensitive to climate change [15], which is expected to affect forest growth, productivity, mortality, distribution, and biodiversity [16]. Specifically, the rise in CO\({}_{2}\) concentration may have impacts on the vegetation NPP through modifying photosynthesis [17]. Therefore, quantifying the vegetation NPP and the relationship between the NPP and climate change is necessary for the carbon cycle and vegetation management. Currently, several previous studies have investigated the NPP and climate change over Northeast China. For example, [PERSON] et al. [18] estimated the monthly NPP of the Northeast China Transcet from 1982 to 2000 and analyzed the response of the NPP to climate change. [PERSON] et al. [15] quantified the response of forest carbon to future climate change in Northeast China. Both studies suggested that climate change will increase the vegetation NPP in future decades. However, these studies were limited to the validation of regional NPP and were focused on the simultaneous interactions between meteorological data and vegetation indicators. The dynamics of the NPP are substantially linked to climatic variations [19]. Additionally, the time-lag effects of vegetation NPP responses to climatic factors should be considered for the comprehensive analysis over Northeast China [20; 21].  
Therefore, we proposed this study to (1) model the vegetation NPP over Northeast China using the BEPS model, (2) understand the spatial and temporal patterns of the NPP from 2003 to 2012, and (3) investigate the response of the NPP to climatic factors (temperature, precipitation, radiation, and humidity).