AbstractSurface solar irradiance varies on scales as small as seconds or metres. This variability is driven mostly by wavelength‐dependent scattering by clouds, and to a lesser extent by aerosols and water vapour. The highly variable nature of solar irradiance is not resolved by most atmospheric models, yet it affects, most notably, the land–atmosphere coupling and the quality of solar energy forecasting. Characterising variability, understanding the mechanisms, and developing models capable of resolving it accurately requires spatially and spectrally resolved observational datasets of solar irradiance at high resolution, which are rare. In 2021, we deployed a network of low‐cost radiometers in the Field Experiment on submesoscale spatio‐temporal variability in Lindenberg (FESSTVaL, Germany) and Land surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment (LIAISE, Spain) field campaigns to gather data on cloud‐driven surface patterns of irradiance, including spectral effects, with the aim of addressing this gap in observations and understanding. We find in case studies of cumulus, altocumulus, and cirrus clouds that these clouds generate large spatiotemporal variability in irradiance, but through different mechanisms and at different spatial scales, ranging from 50 m to 30 km. Spectral irradiance in the visible range varies at similar scales, with significant blue enrichment in cloud shadows, most strongly for cumulus, and red enrichment in irradiance peaks, particularly in the case of semitransparent clouds or near cumulus cloud edges. Under clear‐sky conditions, solar irradiance varies significantly in water‐vapour absorption bands at the minute scale, due to variability in atmospheric moisture in the boundary layer. With this study, we show that observing detailed spatiotemporal irradiance patterns is possible using a relatively small, low‐cost sensor network, and that such network observations can provide insight and validation for the development of models capable of resolving irradiance variability.