I support the designation of this manuscript as a Highlighted Paper. The work presents a substantial community resource that goes beyond a software implementation by establishing a standardized, reproducible framework for disdrometer data sharing and analysis. Given its potential to facilitate future observational, microphysical, and remote sensing studies, I expect it will have broad and lasting impact within the atmospheric sciences.

Methane is the second most important anthropogenic greenhouse gas, responsible for approximately 0.6°C of global warming since pre-industrial times. As one of the world’s largest methane emitters, China requires an expanded observation network to support accurate emission quantification and effective mitigation. However, determining the optimal placement of new monitoring stations remains a major challenge. This study has successfully addressed this challenge by developing an integrated optimization framework, which combines Bayesian inverse modeling with a simulated annealing algorithm, to identify the most effective locations for future methane observations. The results show that prioritizing stations in eastern and southwestern China maximizes information gain, and that adding 50 strategically located sites could nearly double the current capability to constrain national methane emissions. By providing a quantitative roadmap for the design of future methane monitoring networks, this approach is broadly transferable to other rapidly developing regions, offering valuable guidance for global climate mitigation efforts.