## 4 Discussion
### Limitations  
A fundamental source of uncertainty in this study stems from the atmospheric forcing data utilized to drive the model simulations. We employed the standardized FLUXNET2015 dataset, a composite product where gaps in in situ meteorological observations are filled with downscaled data from the ECMWF reanalysis. While this procedure is essential for creating the complete and continuous time series required for land surface model simulations, it inevitably introduces a degree of uncertainty. These potential discrepancies arise from two primary sources, including (1) the spatial scale mismatch between the coarse-resolution reanalysis grid and the point-scale flux tower footprint, and (2) the inherent systematic biases within the reanalysis model itself. Consequently, a portion of the model-data error reported herein must be attributed to this forcing uncertainty. This is particularly relevant for variables like precipitation, where errors in timing and intensity can propagate directly into the simulation of soil moisture dynamics and, subsequently, the surface fluxes of water and carbon. Quantitatively partitioning the total simulation error between forcing data uncertainty and model structural deficiencies is a persistent challenge in the field of land surface modeling, and remains beyond the scope of this evaluation [53; 54]. Furthermore, the geographical distribution of the FLUXNET sites is imbalanced, leading to insufficient ecological representation. The 105 selected sites are primarily concentrated in western Europe and north America, with a significant lack of coverage in tropical rainforests like the Amazon and southeast Asia, and cold regions such as Siberia and the Tibetan Plateau. This uneven distribution may limit the comprehensive evaluation of the model's adaptability to high-radiation, high-humidity, or extreme low-temperature environments. Recent studies underscore that tropical ecosystem environments present unique modeling challenges that differ substantially from temperate systems. For instance, research in seasonally dry tropical forests and in the Amazon rainforest has highlighted the critical role of deep rooting systems, plant hydraulic strategies, and complex phenological responses to seasonal water availability in controlling land-atmosphere fluxes. Furthermore, studies have demonstrated that standard model parameterizations often fail to capture the resilience of these ecosystems to drought, leading to significant errors in GPP and ET simulations [55; 56; 57; 58]. Additionally, some vegetation types (e.g., DNF and OSH) have very few representative sites or site-years, potentially leading to larger uncertainties and less robust conclusions for those types. Additionally, our analysis is primarily stratified by vegetation type rather than by background climate. The geographical distribution of the FLUXNET sites means that certain vegetation types are inherently confounded with specific climate zones (e.g., savannas in water-limited regions, certain forests in temperate zones). A dedicated analysis segmented by a climate classification system would be necessary to disentangle the effects of vegetation physiology from those of overarching climatic drivers (e.g., aridity, temperature limitations). Such an investigation would better clarify the model's structural weaknesses under specific environmental stresses, and remains a critical avenue for future research. A further limitation is that the model does not account for human activities like irrigation and fertilization in agricultural ecosystems. The absence of agricultural management modules, such as those for irrigation and fertilization, results in significant simulation biases for CRO. This is evidenced by a substantial absolute error of 503.21 gC-m\({}^{-2}\)-yr\({}^{-1}\) and a relative error of 43.31% for GPP in agricultural areas. Finally, our parameter sensitivity analysis was limited to predefined scheme combinations and did not include parameter optimization experiments.