Algal blooms represent ecosystem state shifts that degrade drinking water, restrict recreation, threaten public health, and lower property values. Detecting blooms in advance on management relevant timescales of days to weeks can support proactive intervention. Early warning statistics derived from indicator time series offer a framework for detecting state shifts, but the use of lake metabolic estimates (gross primary production, ecosystem respiration, and net ecosystem production) in this context has been limited. Here, we modeled lake metabolism using water-quality monitoring data from eight U.S. lakes spanning a trophic gradient to test the hypothesis that metabolic estimates serve as effective early warning indicators of algal blooms. Time series of early warning statistics were analyzed using the conventional Kendall’s tau approach and a complementary threshold-based approach. The threshold-based approach yielded higher bloom detection rates, longer early warning lead times, and fewer false alarms than the Kendall’s tau approach. Across lakes, metabolic metrics detected 86% of blooms, with the strongest early warning signals in oligotrophic, eutrophic, and hypereutrophic systems. However, directly measured water-quality parameters, particularly water temperature and chlorophyll concentration, were the primary early warning indicators of blooms. Overall, these results demonstrate that metabolic estimates can provide early warning signals of bloom development but are secondary to directly measured water-quality parameters, indicating a complementary rather than primary role in early bloom detection.