We present a climatology of trade cumulus cold pools and their associated changes in surface weather, vertical velocity and cloudiness based on more than 10 years of in situ and remote sensing data from the Barbados Cloud Observatory. Cold pools are identified by abrupt drops in surface temperature, and the mesoscale organization pattern is classified by a neural network algorithm based on Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) infrared images. We find cold pools to be ubiquitous in the winter trades – they are present about 7.8 % of the time and occur on 73 % of days. Cold pools with stronger temperature drops (ΔT) are associated with deeper clouds, stronger precipitation, downdrafts and humidity drops, stronger wind gusts and updrafts at the onset of their front, and larger cloud cover compared to weaker cold pools, which agrees well with the conceptual picture of cold pools. The rain duration in the front is the best predictor of ΔT and explains 36 % of its variability. The mesoscale organization pattern has a strong influence on the occurrence frequency of cold pools. Fish has the largest cold-pool fraction (12.8 % of the time), followed by Flowers and Gravel (9.9 % and 7.2 %) and lastly Sugar (1.6 %). Fish cold pools are also significantly stronger and longer-lasting compared to the other patterns, while Gravel cold pools are associated with significantly stronger updrafts and deeper cloud-top height maxima. The diel cycle of the occurrence frequency of Gravel, Flowers, and Fish can explain a large fraction of the diel cycle in the cold-pool occurrence as well as the pronounced extension of the diel cycle of shallow convection into the early afternoon by cold pools. Overall, we find cold-pool periods to be ∼ 90 % cloudier relative to the average winter trades. Also, the wake of cold pools is characterized by above-average cloudiness, suggesting that mesoscale arcs enclosing broad clear-sky areas are an exception. A better understanding of how cold pools interact with and shape their environment could therefore be valuable to understand cloud cover variability in the trades.