Monthly Soil Respiration in China's Forests: Spatiotemporal Patterns and Drivers Over 21 Years

ABSTRACT Forests play a critical role as terrestrial carbon sinks in mitigating climate change. However, accurate quantification of soil respiration ( R s )—the primary CO 2 efflux from forests—remains challenging due to existing studies' overreliance on annual‐scale estimates, which obscure fine‐scale spatiotemporal dynamics and key drivers of R s . Here, we developed a 500 m resolution monthly R s dataset for China's forests (2000–2020) using remote sensing data and a geographically weighted machine learning model. A geographically weighted extreme gradient boosting model achieved the highest accuracy in predicting monthly R s ( R 2 = 0.74, RMSE = 0.8 g C m −2 day −1 ). The mean total annual R s from 2000 to 2020 was 2.32 ± 0.09 Pg C year −1 , with summer contributing most and winter least. Annual total R s and seasonal total R s showed significant increasing trends across China's forests from 2000 to 2020, with the strongest increases in southern China's young/middle‐aged natural management forests and plantations. The relationships between R s and its driving factors varied: gross primary productivity (GPP) was the primary driver of annual R s across all forest management and age classes. Seasonally, temperature dominated R s in spring/winter, and GPP dominated summer R s across all forest management and age classes, while precipitation effects varied with management/age classes and season. Our findings highlight the necessity of monthly‐scale analysis and the significant role of forest management and age in modulating R s variability.