Spatially‐Uneven Observations Cause Overestimation of Global Soil Respiration

Spatial unevenness in observation sites may hinder accurate global soil respiration (R S ) quantification. We quantified site representativeness using Voronoi volumes within an environmental feature space and implemented an iterative pruning strategy to identify a representative‐optimal subset (1363 sites). Using process‐based models as benchmarks, we developed a Representative‐optimal Model (RM) to simulate global R S dynamics (1982–2022). Compared to the Full‐set model (FM), the RM significantly enhanced spatial consistency with benchmarks and improved alignment with FLUXNET observations and atmospheric CO 2 fluctuations. Neglecting representativeness led to a 6.4 PgC year −1 overestimation of global R S (94.1 vs. 100.5 PgC year −1 ), while simultaneously underestimating its long‐term trend and interannual variability. Arid zones contributed > 50% of this downward revision, reflecting corrected biases in water‐limited regions. These findings demonstrate that optimizing existing dataset representativeness can effectively mitigate systematic biases and refine global carbon budget accounting. While expanding observational networks remains a long‐term priority, we advocate routinely integrating spatial representativeness optimization into data‐driven workflows to immediately correct structural sampling biases.