This paper is concerned with the choice of clear-sky model in solar forecasting. This issue is discussed from three perspectives: (1) accessibility, (2) forecast performance, and (3) statistical properties. Accessibility refers to the time and effort involved in obtaining clear-sky irradiance through a clear-sky model. Forecast performance is analyzed through a new concept called “mean square error (MSE) scaling,” which allows one to decompose the MSE of reference irradiance forecasts into three terms, each carrying a notion of predictability. The decomposition, however, resides on the assumption that the clear-sky index time series is stationary. In this regard, the stationarity assumption is investigated using statistical hypotheses. It is found that even the best clear-sky models, such as the REST2 model, are not able to produce a stationary clear-sky index time series. Instead, the time series is only local stationary, which, in the present context, means that its statistical properties change slowly with the value of clear-sky irradiance. Contrary to the common belief that a better clear-sky model leads to better forecasts, no evidence suggests that the more intricate REST2 has an advantage over the simpler Ineichen–Perez model, in terms of forecast performance. In that, accessibility becomes the primary concern when opting a clear-sky model for forecasting purposes. At this point, the McClear model, available as a web service for worldwide locations at 1-, 15-, and 60-min resolutions, is highly recommended.

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