Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH source, estimates of global wetland CH emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH year ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.