Reductions in methane (CH<sub>4</sub>) emissions are essential to mitigate near-term climate forcing and enable jurisdictions such as New York State (NYS) to meet greenhouse-gas (GHG) reduction targets. This requires quantification of present-day emissions, underpinned by a robust, independent monitoring framework capable of verifying reported reductions. Assessing whether existing observational infrastructure meets these requirements is essential for emissions accounting and policy implementation. Here, we evaluate the ability of stationary <em>in situ</em> methane observations, coupled with a Bayesian inverse modeling framework – the Integrated Methane Inversion (IMI) driven by chemistry-transport model GEOS-Chem – to constrain annual methane emissions and trends across NYS for 2018–2024. We develop capacity for the IMI to assimilate hourly measurements from 29 sites, including four new high-precision continuous monitoring stations calibrated according to international standards. Prior methane emissions for NYS are based on the Gridded New York State (GNYS) inventory, which disaggregates the official NYS bottom-up total state methane emissions estimate for 2020. The observational network provides its strongest constraints around the New York City metropolitan area. Aggregated NYS posterior methane emissions are consistent with the GNYS prior within estimated uncertainties; however, we identify localized emission biases, including underestimates in the New York City area (<em>p</em> < 0.05) and broad overestimates throughout most of NYS. We detect statistically significant regional trends in select locations, but no robust statewide trend over 2018–2024. These results demonstrate the strengths and limitations of stationary <em>in situ</em> monitoring for policy-relevant methane verification and underscore the need for top-down strategies that integrate observations from multiple platforms.