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Nighttime lights (NTLs) provide an unparalleled view for understanding urban environments. The high-frequency NTL intensity changes (NTLICs) and their land drivers in megaregions are less investigated. Here, monthly Visible Infrared Imaging Radiometer Suite-Day Night Band (VIIRS-DNB) NTL images from 2014 to 2020 were regarded as time series modeling input data. Furthermore, the 7-year mean values of five types of Points of Interest Density (POID), Road Density (RD), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) were used to extract features as land drivers of NTLIC. A simplified version of the Breaks For Additive Season and Trend (BFAST01) algorithm was adopted to detect major changes in monthly NTL intensity at the pixel level in the Beijing&#x2013;Tianjin&#x2013;Hebei (BTH) megaregion, China. Geographically weighted regression (GWR) was used to address the spatial nonstationarity of the land drivers. The results showed that (1) increasing trends (63.35&#x0025;) and decreasing trends (4.57&#x0025;) were mainly observed in central and coastal cities, respectively. This indicates that the spatial characteristics of the NLTIC were unbalanced; (2) reversal change type 8 (37.29&#x0025; of abrupt changes) was the dominant type, mainly occurring around April 2017, which may be related to the non-capital function redistribution policy; (3) NDVI and RD were the best one of two physical drivers and six human activity drivers, respectively; and (4) the <italic>R</italic>² of the GWR reached 0.60, a 20&#x0025; improvement over ordinary least squares regression. This study provides an insightful understanding of the dynamics of NTL intensity and its response mechanisms in megaregions.