This paper examines two spatio-temporal approaches for short-term forecasting of global horizontal irradiance using gridded satellite-derived irradiances as experimental support The first approach is a spatio-temporal vector autoregressive (STVAR) model combined with a statistical process for optimum selection of input variables. The second is an existing operational cloud motion vector (CMV) model. An evaluation of the predictive performance of these models is presented for a case study area in the Caribbean Islands. This region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting context. Using scaled persistence as a reference, we benchmark the performance of the two spatio-temporal models over an extended 220 x 220 km domain, and for three specific, climatically distinct locations within this domain. We also assess the influence of intra-day solar resource variability on model performance. Finally, we present preliminary evidence that a blend of CMV and STVAR forecasts leads to improved accuracy under all conditions.