Mountain forest greenness varies across seasons, yet growing-season averages can mask season-specific relationships with climate variability. We assessed seasonal and elevational variation in historical forest greenness within areas classified as forest in 2024, temporal variation in spring NDVI–climate relationships, and the association between nighttime light (NTL) intensity and forest greenness in Changbai Mountain from 2000 to 2024. We used MODIS-derived normalized difference vegetation index (NDVI), 1 km temperature and precipitation datasets, elevation zones, and annual nighttime light (NTL) data. We applied a forest mask derived from the 2024 Annual China Land Cover Dataset (CLCD) and retained 1 km pixels with at least 70% forest cover. NDVI increased significantly over the aggregate growing season (slope = 0.0011 NDVI yr⁻¹, R² = 0.584, p < 0.001), summer (slope = 0.0006 NDVI yr⁻¹, R² = 0.188, p = 0.030), and autumn (slope = 0.0019 NDVI yr⁻¹, R² = 0.280, p = 0.007). Autumn showed the largest estimated slope; this ranking alone does not establish statistical differences among seasonal slopes. Spring showed no significant long-term trend (slope = 0.0011 NDVI yr⁻¹, R² = 0.087, p = 0.152), but spring NDVI showed a strong positive correlation with temperature at the regional scale (r = 0.760, p < 0.001), with positive correlations maintained across all elevation zones. In spring regression models, temperature showed a larger standardized coefficient magnitude than precipitation across elevation zones, with all variance inflation factors ≤ 1.22. Exploratory five-year sliding-window correlations suggested temporal variation in spring NDVI–climate relationships, although each estimate was based on only five annual observations. At low elevation, the NTL–NDVI regression was significant (R² = 0.198, p = 0.026), whereas associations at middle and high elevations were not statistically significant; this raw association should be interpreted cautiously because both variables may contain concurrent temporal trends. These results describe seasonal and elevational greenness patterns within the 2024 forest mask, highlighting the distinct spring temperature association and elevation-dependent relationships between NTL intensity and forest greenness.

Mountain forest greenness varies across seasons, yet growing-season averages can mask season-specific climate associations. We assessed seasonal and elevational variation in forest greenness, temporal variation in spring climate associations, and whether nighttime light (NTL)–greenness covariation persisted after detrending in Changbai Mountain from 2000 to 2024. Analyses used MODIS normalized difference vegetation index (NDVI), 1 km temperature and precipitation grids, elevation zones, and annual NTL data, restricted to pixels with at least 70% forest cover based on the 2024 Annual China Land Cover Dataset. Forest NDVI increased significantly during the growing season (slope = 0.0006 yr⁻¹, R² = 0.166, P < 0.05), summer (slope = 0.0006 yr⁻¹, R² = 0.188, P < 0.05), and autumn (slope = 0.0019 yr⁻¹, R² = 0.280, P < 0.01), with the steepest increase in autumn. Spring showed no significant long-term trend (slope = 0.0011 yr⁻¹, R² = 0.087, P = 0.152), but its NDVI had the strongest temperature association at the regional scale (r = 0.760, P < 0.001), remaining positive across all elevation zones. In spring regressions, temperature represented 84.1–97.8% of the summed absolute standardised coefficient magnitude across elevation zones, with all variance inflation factors ≤1.22. Exploratory 5-year sliding-window correlations suggested that spring NDVI–climate correlations varied over time, but each estimate was based on only five observations. At low elevation, the raw NTL–NDVI regression was significant (R² = 0.198, P < 0.05), whereas the detrended regression was not (R² = 0.042, P = 0.324), indicating shared long-term trends. Growing-season averaging therefore obscures strong autumn greening and the distinct spring temperature association; raw NTL–NDVI covariation should not be interpreted without accounting for temporal trends.