Seasonal water temperature data from 388 large Canadian lakes (area ≥ 100 km2) were used to develop improved empirical tools for forecasting the impacts of climate change on the magnitude (TP) and time of occurrence (JP) of annual peak surface water temperatures. Analyses of remotely sensed open-water temperatures with sinusoidal models produced estimates of TP and JP predominately better than other models. Those estimates were analyzed for lake and climate patterns. Linear mixed effects regression produced a significant model of TP with fixed positive effects for mean July and annual air temperatures and lake perimeter, but negative effects with mean July and annual percent cloud cover, mean annual precipitation, range of monthly mean global clear sky radiation, area, and elevation. Subsets of the estimates with mean, maximum, or Secchi depth values produced similarly significant models with negative depth coefficients. JP was relatively invariant but small, significant lake and climate effects were detected. The best models identified in our analyses will be useful tools for forecasting how climate change will alter aspects of the limnetic seasonal water temperature cycle that strongly influences the species composition and productivity of their fisheries.