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Abstract Microbial processes are crucial in producing and oxidizing biological methane (CH₄) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH₄cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH₄flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH₄flux. Specifically, the coefficient of determination (R²) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop inR²was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH₄flux, additional constraints of functional gene data led to better CH₄seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH₄production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH₄flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH₄models to better understand microbial roles in CH₄in the era of climate change. 

ABSTRACT The current soil carbon paradigm puts particulate organic carbon (POC) as one of the major components of soil organic carbon worldwide, highlighting its pivotal role in carbon mitigation. In this study, we compiled a global dataset of 3418 data points of POC concentration in soils and applied empirical modeling and machine learning algorithms to investigate the spatial variation in POC concentration and its controls. The global POC concentration in topsoil (0–30 cm) is estimated as 3.02 g C/kg dry soil, exhibiting a declining trend from polar regions to the equator. Boreal forests contain the highest POC concentration, averaging at 4.58 g C/kg dry soil, whereas savannas exhibit the lowest at 1.41 g C/kg dry soil. We developed a global map of soil POC density in soil profiles of 0‐30 cm and 0–100 cm with an empirical model. The global stock of POC is 158.15 Pg C for 0–30 cm and 222.75 Pg C for 0–100 cm soil profiles with a substantial spatial variation. Analysis with a machine learning algorithm concluded the predominate controls of edaphic factors (i.e., bulk density and soil C content) on POC concentration across biomes. However, the secondary controls vary among biomes, with solid climate controls in grassland, pasture, and shrubland, while strong vegetation controls in forests. The biome‐level estimates and maps of POC density provide a benchmark for modeling C fractions in soils; the various controls on POC suggest incorporating biological and physiochemical mechanisms in soil C models to assess and forecast the soil POC dynamics in response to global change.