Satellite derived precipitation data are usually a combination of one or several precipitation data with estimates derived from satellite imagery. The temporal and spatial resolution depends on the instrument used in their production. Except for geostationary satellites, most satellites give near-global coverage on some repeat cycle. For example, some satellites may sample at least once per 100 km grid cell every sixteen days (CloudSat). Depending on the orbit, they will have more frequent sampling at some latitudes than others. Here we consider only datasets that have coverage up to at least 60 ̊N. However, a single satellite does not fly over a region more than twice per day, and the gaps between overpasses may miss short-lived precipitation events. This is why merged or “hybrid” precipitation datasets are useful, as they can combine the observations from multiple satellite platforms to fill in some of the gaps. Typically, passive microwave or infrared instruments are used to estimate precipitation. Infrared sensors estimate cloud top temperatures, and an algorithm is used to relate the measured temperature to a precipitation estimate. Microwave-based algorithms derive precipitation from both scattering and emission from hydrometeors and cloud droplets. Note that emission cannot be used to estimate precipitation over land due to the heterogeneity of the surface emissivity. Low intensity and short duration precipitation events tend to be under-sampled. Currently, there are no studies that evaluate those datasets over the Canadian North.