Gap-filling XCO₂ variability: A conditional framework to estimate XCO₂ in data-sparse regions using dense TROPOMI NO₂...

Accurate monitoring of fine-scale anthropogenic carbon dioxide (CO₂) variability is crucial for urban emission assessment but remains constrained by the sparse spatial sampling of dedicated satellites like Orbiting Carbon Observatory-2 and -3 (OCO-2/3). Nitrogen dioxide (NO₂), co-emitted with CO₂ and observed daily by TROPOspheric Monitoring Instrument (TROPOMI), offers a potential pathway to inform CO₂ patterns where direct measurements are lacking. This study addresses a central challenge in leveraging this link: rather than seeking a universal relationship, we rigorously define the specific spatiotemporal conditions under which TROPOMI NO₂ can reliably estimate XCO₂ variability in data-sparse regions over China (2020-2024). Our analysis reveals a fundamental decoupling in long-term trends (NO₂ stable, XCO₂ rising) yet identifies a significant positive correlation (r = 0.344, p = 0.008) in their short-term, de-seasonalized relative changes. Crucially, this correlation is strongly conditional: it peaks during winter (r > 0.60) and is significantly enhanced within intense emission hotspots like the Yangtze River Delta. Building on this empirical mapping, we develop an integrated framework that quantifies the modulation by season and region. A proof-of-concept application demonstrates its potential for generating observationally informed estimates of XCO₂ changes in areas unsampled by current satellites. Our work provides the essential foundation for developing reliable, context-aware gap-filling methods, directly addressing a key limitation in high-resolution urban carbon monitoring.