## 1 Introduction  
Global warming (especially in the most sensitive regions, including Three Polar) and its causes are a great concern for governments and scientists [1; 2; 3; 4; 5; 6]. Global CO\({}_{2}\) concentrations are rising since the Industrial Revolution in the 19 th century. CO\({}_{2}\) accounts for 70% of greenhouse gas (GHG) emissions [7], and its emission should be critically controlled in the processes to achieve regional and global carbon neutrality [8; 9]. It is also vital to thoroughly investigate the regional and global ecosystem carbon balance and their long-term variations, as the terrestrial biosphere can sequester 20%-30% of global anthropogenic CO\({}_{2}\) emissions [10]. As a reliable technique, the eddy covariance (EC) is extensively used to measure and validate CO\({}_{2}\) exchange during different time scales (half-hour to several tens of years) at sites with different types of terrain in the world, such as ChinaFLUX, AsiaFlux, AmeriFLUX, and CARBOEUROPE [11; 12; 13; 14; 15; 16; 17; 18; 19]. Flux data are further commonly used in carbon cycle model developments and evaluations, investigating the processes, mechanisms, and potential effects of CO\({}_{2}\) on climate change. Though much progress has been made, there are still challenges in EC measurements and model simulations, especially how to reduce their large uncertainties [20] and how to improve data quality [19; 21; 22]. The problem of surface energy imbalance/nonclosure still needs to be better resolved [23; 24; 25]. Thus, it is important to comprehensively study long-term variations and reasons for carbon balance and its driving factors. Based on measurements of net carbon exchange, many types of models are developed and applied to (1) calculate net ecosystem exchange (NEE), net ecosystem productivity (NEP), respiration (Re), and gross primary production (GPP) [26; 27; 28; 29; 30; 31; 32; 33; 34; 35]; (2) analyze their variations [35; 36; 37; 38; 39; 40; 41]; and (3) investigate their responses to the driving factors (light, temperature, water, drought, land cover change, etc.) [26; 27; 28; 29; 38; 40; 41]. These models include empirical models, sophisticated models [32; 42; 43; 44], and atmospheric inversions [45; 46]. The process-based dynamic global vegetation models (DGVMs) are commonly used to compute the land carbon sink in reasonable line with the global carbon budget. Remote sensing and geographic information system (GIS) data-based methods provide useful tools to study carbon balance. Machine-learning algorithms (e.g., extreme gradient boosting (XGBoost) and artificial neural networks (ANN)) are also applied as promising methods [47; 48; 49]. Although much progress has been made, there are still large differences between different models [44] and large uncertainties in CO\({}_{2}\) flux observations and model predictions [50; 51; 52; 53; 54; 11]. Additionally, many hypotheses are still used in complex models [56]. Respiration is an important part of carbon cycling [57] and is necessary to be well studied, e.g., the partitioning between the components of the CO\({}_{2}\) atmospheric budget [58]. NEP (or NEE) as a significant indicator of ecosystem carbon source and sink should be evaluated as well. The greening pattern during 2000-2017 worldwide is markedly evident in China [59]. To fulfill carbon peak and carbon neutrality, more and more plants and grasses will be cultivated in China. China hosts several typical ecosystems, and great progress has been achieved by Chinese scientists on China's land carbon exchange. For example, the number of sites in the ChinaFLUX network between 2002 and 2022 increased to 81, including 25 forest stations, 19 grassland stations, and 18 agriculture stations. Numerous flux tower measurement and model studies, along with fruitful results, have been conducted [33; 37; 30; 31; 35; 36; 37; 39]. Since 1982, 1.65 \(\pm\) 0.76 PgC has been stored in forest [60].