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Four previously identified patterns of meso‐scale cloud organization in the trades — called Sugar, Gravel, Flowers, and Fish — are studied using long‐term records of ground‐based measurements, satellite observations and reanalyzes. A deep neural network trained to detect these patterns is applied to satellite imagery to identify periods during which a particular pattern is observed over the Barbados Cloud Observatory. Surface‐based remote sensing at the observatory is composited and shows that the patterns can be distinguished by differences in cloud geometry. Variations in total cloudiness among the patterns are dominated by variations in cloud‐top cloudiness. Cloud amount near cloud base varies little. Each pattern is associated with a distinct atmospheric environment whose characteristics are traced back to origins that are not solely within the trades. Sugar air‐masses are characterized by weak winds and of tropical origin. Fish are driven by convergence lines originating from synoptical disturbances. Gravel and Flowers are most native to the trades, but distinguish themselves with slightly stronger winds and stronger subsidence in the first case and greater stability in the latter. The patterns with the higher cloud amounts and more negative cloud‐radiative effects, Flowers and Fish, are selected by conditions expected to occur less frequently with greenhouse warming. ; Key Points: Meso‐scale patterns of trade‐wind clouds are identified with a neural network and characterized based on observations. The four analyzed patterns distinguish themselves by stratiform cloudiness and less by cloudiness at the lifting condensation level. Two patterns are imprinted by tropical, respectively extra‐tropical intrusions. ; European Union's Horizon 2020 Research and Innovation Programme ; NASA ; https://doi.org/10.5281/zenodo.4767674