Associations Between Hydroclimatic Extremes and Tropospheric NO2 Anomalies Across Land-Use Backgrounds in Northeast China’s Black Soil Region

Tropospheric NO2 anomalies reflect meteorological, land-surface, and emission influences, yet whether greater anomaly activity indicates stronger associations with hydroclimatic extremes across landscapes remains unclear. We analyzed monthly TROPOMI NO2 anomalies (2019–2025) at 10 km resolution across paddy, dryland, forest, and grassland backgrounds in Northeast China’s black soil region, where typical black soils mainly correspond to Phaeozems and Chernozems. We assessed anomaly activity, concurrent and lagged correlations, and regression-based heat- and precipitation-related associations with paired spatial-block bootstrap inference. Agricultural backgrounds exhibited greater anomaly magnitudes, high-anomaly frequencies, and growing-season variability than forest and grassland, with median high-anomaly frequencies of 32.9–34.8% versus 7.5–15.2%. During May–September, regional standardized total associations were positive for heat (0.163; 95% spatial-block CI: 0.116–0.211) and negative for precipitation (−0.063; −0.110 to −0.023). The regional heat association remained positive across robustness checks, but agricultural–non-agricultural heat contrasts were not supported by paired spatial-block confidence intervals, despite grid-cluster bootstrap intervals excluding zero for all four contrasts. Precipitation associations were generally more negative over agricultural backgrounds, although evidence for between-background differences depended on multiple-comparison adjustment. The regional precipitation estimate remained negative under coverage-covariate and minimum-valid-day checks but approached zero under a stringent common-coverage restriction (−0.0035; 95% CI: −0.0522 to 0.0402). These findings distinguish elevated NO2 anomaly activity from evidence of stronger hydroclimatic associations, while documenting a comparatively stable regional heat association and greater coverage sensitivity of precipitation-related estimates. This distinction supports more careful interpretation of atmospheric NO2 variability across heterogeneous agricultural and natural landscapes.