Abstract. Downwelling surface shortwave flux (DSSF) is a key parameter to address many climate, meteorological, and solar energy issues. Under clear sky conditions, DSSF is particularly sensitive to the variability both in time and space of the aerosol load and chemical composition. Hitherto, this dependence has not been properly addressed by the Satellite Application Facility on Land Surface Analysis (LSA-SAF), which operationally disseminates instantaneous DSSF products over the continents since 2005 considering unchanging aerosol conditions. In the present study, an efficient method is proposed for DSSF retrieval that will overcome the limitations of the current LSA-SAF product. This method referred to as SIRAMix (Surface Incident Radiation estimation using Aerosol Mixtures) is based on an accurate physical parameterization that is coupled with a radiative transfer-based look up table of aerosol properties. SIRAMix considers an aerosol layer constituted of several major aerosol species that are conveniently mixed to match real aerosol conditions. This feature of SIRAMix allows it to provide not only accurate estimates of global DSSF but also the direct and diffuse DSSF components, which are crucial radiative terms in many climatological applications. The implementation of SIRAMix is tested in the present article using atmospheric inputs from the European Center for Medium-Range Weather Forecasts (ECMWF). DSSF estimates provided by SIRAMix are compared against instantaneous DSSF measurements taken at several ground stations belonging to several radiation measurement networks. Results show an average root mean square error (RMSE) of 23.6 W m−2, 59.1 W m−2, and 44.9 W m−2 for global, direct, and diffuse DSSF, respectively. These scores decrease the average RMSE obtained for the current LSA-SAF product by 18.6%, which only provides global DSSF for the time being, and, to a lesser extent, for the state of the art in matter of DSSF retrieval (RMSE decrease of 10.9%, 6.5%, and 19.1% for global, direct, and diffuse DSSF with regard to the McClear algorithm). In addition to the retrieval of DSSF, SIRAMix is able to quantify the radiative forcing at the surface due to a given atmospheric component (e.g., gases or aerosols). The main limitation of the proposed approach is its high sensitivity to the quality of the ECMWF aerosol inputs, which is proved to be sufficiently accurate for reanalyses but not for forecasted data. This outcome will be taken into account in the forthcoming implementation of SIRAMix in the operational production chain of the LSA-SAF project.