## 1 Introduction  
Terrestrial ecosystems have absorbed approximately 30% of anthropogenic carbon dioxide emissions over the past decade, effectively mitigating global climate warming [1]. China's terrestrial ecosystems have been recognized as a significant component of the global carbon sink [2, 3, 4]. However, due to the diversity of China's climate and ecosystems along with the significant spatial heterogeneity of terrestrial ecosystems, there are still considerable uncertainties in estimating the carbon sink capacity [3]. Reducing these uncertainties is crucial for refining the global carbon budget, supporting the formulation of climate policies, and predicting future climate change. Net ecosystem productivity (NEP), calculated as the difference between net primary productivity (NPP) and soil heterotrophic respiration (HR), is a key indicator for assessing carbon fluxes in terrestrial ecosystems [5]. The estimation of regional terrestrial ecosystem carbon fluxes is typically categorized into 'bottom-up' and 'top-down' approaches [3]. Terrestrial ecosystem models, such as the widely used Carnegie-Ames-Stanford Approach (CASA) model, are common 'bottom-up' methods and are important tools for assessing carbon sinks on global and regional scales [6,7]. These models are usually driven by inputs, such as climate variables and land-use change data, and can predict future changes in ecosystem carbon sinks [3]. However, the factors driving carbon sinks in these models are often not fully comprehensive [3]. Recently, an increasing number of studies have highlighted the significant impact of changes in the growing season length of temperate plants on ecosystem productivity [8,9]. While warmer authumns may increase carbon loss by extending respiratory activity, the advance in the start of the growing season (SOS) and the delay in the end of the growing season (EOS) caused by climate warming have extended the carbon uptake period and increased the net carbon uptake of Northern Hemisphere forest ecosystems [8]. Therefore, incorporating vegetation phenology information into ecosystem models is crucial for accurately assessing vegetation productivity. Traditional plant phenology models typically use accumulated temperature to determine EOS [10,11,12]. However, the drivers of autumn phenology are more complex, being influenced not only by climatic conditions but also by the developmental status of plants, including the growing season gross primary production (GPP) and SOS [9,10,12]. Developmental factors can influence plant senescence through direct mechanisms, such as carbon sink limitations from photosynthesis and programmed cell death, as well as indirect mechanisms, like intensified summer drought [13,14]. Studies have shown that warming-induced advances in SOS and increased growing season GPP may lead to earlier plant senescence, partially offsetting the delaying effect of warming on EOS [9,15]. Furthermore, if phenology models overlook plant developmental factors, they may overestimate future delays in autumn phenology, thereby inflating projections of future ecosystem productivity [9,12]. Integrating phenology algorithms into GPP models has been demonstrated to significantly enhance model accuracy and reduce biases compared to flux tower-measured GPP [11]. Therefore, improving phenology models and integrating their outputs with NPP models is essential for achieving more accurate regional estimates of NEP. In this study, we proposed an improved autumn phenology model and used it to constrain the output of the CASA model, aiming to enhance the prediction accuracy of NEP for China's terrestrial ecosystems. The objectives of this study were: (1) to construct an autumn phenology model incorporating developmental factors; (2) to assess the NPP simulation performance of the CASA model modified by incorporating phenology information; and (3) to analyze the spatial patterns and temporal trends of NEP in China from 2003 to 2021 based on the phenology-modified CASA model.