There is growing concern that harmful cyanobacterial blooms are increasing in frequency and occurrence around the world. Although nutrient enrichment is commonly identified as a key predictor of cyanobacterial abundance and dominance in freshwaters, several studies have shown that variables related to climate change can also play an important role. Based on our analysis of the literature, we hypothesized that temperature or water-column stability will be the primary drivers of cyanobacterial abundance in stratified lakes whereas nutrients will be the stronger predictors in frequently mixing water bodies. To test this hypothesis, as well as quantify the drivers of cyanobacteria over different scales and identify interactions between nutrients and climate-related variables, we applied linear and nonlinear mixed-effect modeling techniques to seasonal time-series data from multiple lakes. We first compared time series of cyanobacterial dominance to a published lake survey and found that the models were similar. Using time-series data of cyanobacterial biomass, we identified important interactions among nutrients and climate-related variables; dimictic basin experienced a heightened susceptibility to cyanobacterial blooms under stratified eutrophic conditions, whereas polymictic basins were less sensitive to changes in temperature or stratification. Overall, our results show that due to predictable interactions among nutrients and temperature, polymictic and dimictic lakes are expected to respond differently to future climate warming and eutrophication.