Accurate forecasting of Global Horizontal Irradiance (GHI) is essential for the integration of the solar resource in an electrical grid. We present a novel data-driven method aimed at delivering up to 6 h hourly probabilistic forecasts of GHI on top of a localized solar energy source. The method does not require calibration to adapt to regional differences in cloud dynamics, and uses only one type of data, covering Europe and Africa. It is thus suited for applications that require a GHI forecast for solar energy sources at different locations with few ground measurements. Cloud dynamics are emulated using an analog method based on 5 years of hourly images of geostationary satellite-derived irradiance, without using any numerical prediction model. This database contains both the images to be compared to the current atmospheric observation and their successors at one or more hours of interval. The physics of the system is emulated statistically, and no numerical prediction model is used. The method is tested on one year of data and five locations in Europe with different climatic conditions. It is compared to persistence (keeping the last observation frozen), ensemble persistence (generating a probabilistic forecast using the last observations) and an adaptive first order vector autoregressive model. As an application, the model is downscaled using ground measurements. In both cases, the analog method outperforms the classical statistical approaches. Results demonstrate the skill of the method in emulating cloud dynamics, and its potential to be coupled with a forecasting algorithm using ground measurements for operational applications.