Wetland CH₄ emissions are among the most uncertain components of the global CH₄ budget. The complex nature of wetland CH₄ processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH₄ emissions. In this study, we used the flux measurements of CH₄ from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH₄ emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH₄ emissions in all studied wetland types. Ecosystem respiration (CO₂) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH₄ emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH₄ emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH₄ emissions within earth system land models.

湿地 CH ₄排放是全球 CH ₄预算中最不确定的组成部分之一。_{湿地 CH 4}过程的复杂性使得确定因果关系以提高我们对 CH ₄排放的理解和可预测性具有挑战性。在这项研究中，我们使用来自涡协方差塔（来自 4 种湿地类型的 30 个地点：沼泽、沼泽、沼泽和湿苔原）的 CH ₄通量测量来构建因果约束机器学习 (ML) 框架来解释调节因子并在次季节尺度捕获 CH _4排放。我们发现土壤温度是 CH _{4的主导因素}所有研究的湿地类型的排放。生态系统呼吸 (CO ₂ ) 和总初级生产力在沼泽、沼泽和沼泽地发挥控制作用，反应滞后数天到数周。将这些异步环境和生物因果关系整合到预测模型中可显着提高模型性能。更重要的是，当考虑因果约束时，在 +1°C 升温情景下模拟的 CH ₄排放差异高达 4 倍。这些结果突出了因果关系在模拟湿地 CH _{4中的重要作用}排放量，尤其是在未来变暖条件下，而传统的数据驱动的 ML 模型可能会出于错误的原因重现观测结果。我们提出的因果关系引导模型有助于地球系统土地模型中湿地 CH ₄排放的预测建模、大规模升级、数据填补和替代建模。