Be aware of the significance of land surface net radiation ( $R_{n}$ ), there is a need for accurate long-term and high spatial resolution global $R_{n}$ estimates based on satellite data. Herein, we propose a novel globally applicable, highly effective algorithm for estimating daily $R_{n}$ directly from Visible Infrared Imaging Radiometer Suite (VIIRS) top-of-atmosphere (TOA) observations ranging from 2011 to present, using the eXtreme Gradient Boosting (XGBoost) method. This algorithm, named the constraint conditional model (CCM), consists of five conditional models (namely, cases 1–5 model) divided by the combination of the length of daytime (dt), the instantaneous sky condition, and the surface broadband albedo, and the daily downward shortwave radiation (DSR) from ERA5-Land was introduced as a physical constraint when $dt \gt 9$ , in which case $R_{n}$ is dominated by $R_{\textit {si}}$ (incoming solar radiation). The validation accuracy of CCM was satisfactory against the ground measurements, yielding a root-mean-square error (RMSE) of 18.95 Wm−2, a bias of 0.056 Wm−2, and an $R^{2}$ of 0.89. The algorithm exhibited superior accuracy and robustness compared to GLASS-MODIS and ERA5-Land under spatiotemporally independent validation samples. This indicates the potential of VIIRS to extent MODIS $R_{n}$ products for generating long-term global daily $R_{n}$ data.