Accurate estimation of surface net longwave radiation (LW_net) and turbulent fluxes (Shle) is crucial for understanding the mechanisms of surface energy balance (SEB) and modeling SEB-based approach for estimating land surface temperature (LST) under cloudy sky. Parameterized or empirical regression methods based on remotely sensed data are effective ways to acquire LW_net and Shle. Current methods are always limited in global application due to the scarcity of observation sites and the absence of remote-sensing observations under cloudy conditions. To overcome these issues, this study developed multiple linear regression models (MLR) based on the data from 62 global sites covering 12 International Geosphere-Biosphere Program (IGBP) land cover types to estimate LW_net and Shle, in which net shortwave radiation (SW_net), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and digital elevation model (DEM) were used as variables. Model performance was evaluated with an independent dataset at both overall and seasonal scales. Then, the models were applied to remote-sensing products to estimate all-weather LW_net and Shle at a spatial resolution of 500 m, and the estimates were assessed against in situ data from five sites located in semiarid and arid regions in Northwest China. The results showed that the introduction of NDMI significantly improved prediction accuracy for most land cover types. The root-mean-square error (RMSE) of predicted LW_net ranged from 18.57 to 29.13 W/m(2) with a mean RMSE of 25.65 W/m(2), and the error of Shle ranged from 51.45 to 103.79 W/m(2) with a mean RMSE of 65.16 W/m(2). Application of the models to remote-sensing products, in which SW_net was provided by FY-4B surface shortwave radiation product, showed that the RMSE of estimated LW_net was 27.13 W/m(2) in summer, 25.46 W/m(2) in autumn, and 17.14 W/m(2) in winter. For Shle, the average RMSE was 65.07 W/m(2) in summer, 39.25 W/m(2) in autumn, and 19.38 W/m(2) in winter. Although the accuracy declined slightly over complex vegetation types and in the summer, the models exhibited robust applicability across different land cover types and seasons. This study provides an efficient, generalized method for estimating LW_net and Shle, which is promising to be used for studies on energy balance at regional and global scales and retrieval of LST under cloudy skies using SEB-based method.