AbstractSatellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH‐2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up‐scaling methods based on SARAH‐2 in the validation of degree‐scale products. The fully data‐driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH‐2 uncertainty to the corrections. The model‐based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high‐resolution data set and depends less SARAH‐2 uncertainty.