## 1 Introduction  
Forests are the main part of terrestrial ecosystems and also the largest carbon storage pool on land [1, 2, 3]. Human-induced climate change could fundamentally alter forests in the 21 st century, with profound implications for the world [4]. Forest biomass is a key indicator in global carbon cycle research [5]. Thinning is a common forestry management practice. It has a significant and direct impact on forest structure. The understory microclimate will change with the change of structure and then affect the growth and development of trees [6; 7]. Through thinning, stand density is reduced, the interception of precipitation is decreased while the understory light level is increased, and the decomposition of forest litter is accelerated [8]. Retained wood can make full use of light, water, and soil, and forest productivity can finally be improved. Accurately estimating the synergistic effect of thinning and climate on forest growth is of great significance for optimizing the utilization of forest resources and improving the ecological quality of forests [9; 10; 11]. In recent years, researchers have extensively explored how to estimate the response of forest biomass to thinning operations and climate change [12; 13]. With the in-depth study of forestry management, mechanistic models have been developed and have received more and more attention [14; 15]. A process model can simulate the key mechanistic processes of photosynthesis, respiration, and carbon water balance in tree growth. A process model takes into account the impact of climate factors and human disturbance on forest growth [16]. Individual-tree process models such as MAESTRA or TRIPLEX are established based on the three-dimensional spatial information of a single tree. These models take into account the extent to which the canopy utilizes solar radiation [17; 18]. Individual-tree process models exhibit a high level of precision in their simulations. However, individual-tree process models rely on field surveys, which can incur higher costs. A stand process model, such as 3-PG or FORECAST, considers the effects of stand structure, climate change, and management practices. This model effectively elucidates and simulates the influence of environmental variables on the growth of forests [19; 20]. Ecosystem process models, such as BIOME-BGC or LPG-DGVM, have comprehensively incorporated the carbon cycle within the atmosphere-vegetation-soil system. Its complexity is higher, and it is more suitable for large-scale carbon-water cycle simulation [21; 22]. The essence of the process model is to complete the description of the material exchange and energy flow processes between the atmosphere, vegetation, and soil with the help of complex empirical equations. However, due to the fact that the environment is everchanging and will continue to change in the future, a model established based on past knowledge may no longer hold true. Therefore, it is difficult to predict dynamic changes in forests on a long-term scale (e.g., 50a). With the development of artificial intelligence technology, Deep Learning (DL) provides a new means for forecasting forest growth and harvest [23; 24]. DL does not restrict the selection of data sources. It is able to automatically extract knowledge from data streams to provide more flexible predictions. [PERSON] et al. [25] used LSTM to build a global model for fitting the Normalized Difference Vegetation Index (NDVI). Their model shows that LSTM can identify the memory effect of vegetation state on climate with satisfactory fitting accuracy. In addition to LSTM, other DL methods are also widely used. [PERSON] et al. [26] used principal component analysis (PCA) to reduce the dimensionality of input data and used an Artificial Neural Network (ANN) to estimate productivity dynamics during Eucalyptus rotations. With a limited sample size, it shows better results than other forest productivity estimation methods. [PERSON] et al. [27] proposed a neural network model with a multi-task loss function.