Characterizing surface radiation fluxes is essential for understanding land–atmosphere energy exchanges and their roles in ecosystem–climate interactions. This study examines the temporal variability and trends of incoming and outgoing shortwave ( \(S_{\downarrow }\) , \(S_{\uparrow }\) ) and longwave ( \(L_{\downarrow }\) , \(L_{\uparrow }\) ) radiation components over a Brazilian Pampa grassland using hourly observations from 2014 to 2023 in Santa Maria, Rio Grande do Sul, Brazil. Empirical models were developed to estimate radiation components using five meteorological variables as predictors. Results showed a discernible seasonal daily cycle, with net radiation ( \(Q^{*}\) ) peaking at \(582.69 \pm 241.04 {\mathrm{W}}\,{\mathrm{m}}^{-2}\) in summer and declining by 46.8% in winter. Shortwave radiation exhibited strong seasonality, decreasing by over 40% from summer to winter, while longwave components showed weaker variation (13–14%). A significant positive trend in summer \(S_{\downarrow }\) ( \(+5.42 {\mathrm{W}}\,{\mathrm{m}}^{-2},{\mathrm{yr}}^{-1}\) , \(p=0.046\) ) and a significant negative trend in \(L_{\downarrow }\) ( \(-1.48\,{\mathrm{W}}\,{\mathrm{m}}^{-2}\,{\mathrm{yr}}^{-1}\) , \(p=0.042\) ) indicate increasing atmospheric transparency and reduced atmospheric emissivity. Seasonal albedo ranges from \(0.166 \pm 0.012\) in summer to \(0.175 \pm 0.015\) in winter that is consistent with grassland surface characteristics. Cloud–radiation analysis provided strong observational evidence that cloud variability exerted a dominant control on surface radiation. Increased cloud cover enhanced downward longwave radiation while reducing incoming shortwave radiation, leading to a net decrease in available surface energy. Seasonal patterns further confirmed that reduced cloudiness corresponds to enhanced solar irradiance at the surface. Multivariate regression results identified surface and air temperatures as dominant predictors of daytime radiation components, while vapour pressure deficit governed nighttime variability, except for \(L_{\uparrow }\) , where air temperature dominated. These findings provide a strong basis for improving regional climate modelling, energy balance assessment, renewable energy applications, and sustainable land management in subtropical ecosystems.
