The added value of new ground-based observations in improving China's methane emission quantification

China is one of the largest anthropogenic methane emitters, yet its current space- and ground-based observational network remains insufficient for robust emission quantification, particularly in southern regions. To address this gap, we develop an integrated framework, employing Bayesian analytical inversion and simulated annealing algorithms, to design optimal ground-based methane monitoring networks. In Bayesian theory, the degrees of freedom for signal (DOFS) is usually used to quantify the number of independent information provided by observations, with higher values indicating stronger constraint capability. Using GEOS-Chem at 50 km resolution, we estimate that current TROPOMI observations and existing surface measurements (13 in situ sites and 4 ground column sites in East Asia) can provide a DOFS of 134 for methane emissions in China. We further assess the performance of networks comprising 5 to 100 new stations across daily, weekly and monthly sampling frequencies. Optimized designs consistently prioritize new sites in southwestern and eastern China, where satellite coverage is sparse and emissions are high. Adding 50 optimally placed stations with weekly sampling can approximately double the DOFS (from 134 to 259). These results highlight the significant potential of combining optimized ground-based networks with satellite data to improve methane emission quantification in China.