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The moistening of cold air passing over the Great Lakes of North America has a profound impact on the cool season climate of regions downwind, from relatively benign air mass modification to highly-impactful snowfall events. The importance of lake effects has led to the development of varying techniques for systematically identifying lake-effect days. The results of two such methods are merged here to yield a more thorough record of lake-effect days for the eastern Great Lakes. Comparative analysis of the data sets illustrates the different objectives of the two methodologies, where one identifies days with a synoptic setup conducive to lake-effect snowfall, and the other identifies days with lake-effect modification of the overlying air mass. A smaller population of “absolute” lake-effect days are those identified by both methods, while a larger population of “hybrid” lake-effect days are absolute days plus those identified by one method but not the other. For a 51-year study period ending with the 2014–15 cool season, the absolute data set yields a mean of about 15 lake-effect days per year, or 8% of the November through April season, while the hybrid data set yields a mean of 56 lake-effect days per year, or 31% of the season. The frequencies of absolute, air mass modification-defined, and hybrid lake-effect days decreased through the study period, with days within the hybrid data set declining at a statistically significant rate of 2.8 days per decade, although most obviously from the late 1970s through the early 2000s. The result is a general drying of the cool-season lake-effect hydroclimate. The merged data set offers a more thorough historical record of days available for atmospheric and hydroclimatic study of the lake-effect phenomenon within the eastern Great Lakes region.

A Hybrid Dataset of Historical Cool-Season Lake Effects From the Eastern Great Lakes of North America