Using models to predict future changes in species distributions in response to projected climate change is a common tool to aid management and species conservation. However, the assumption underlying this approach, that ecological processes remain stationary through time, can be unreliable, and more empirical tests are needed to validate predictions of biotic outcomes of global change. We used contemporary (2003–2019) and historical (1936–1964) abundance datasets of largemouth bass Micropterus salmoides in lakes across Michigan to estimate how relative density has changed over time with changing temperature. We found hindcasting contemporary models to historical lake temperatures performed similarly in predicting historical density to models predicting contemporary density. Our results suggest that models built using spatial environmental gradients can reliably predict population changes through time. We also found that increasing surface water temperature led to increasing largemouth bass relative density across the time period. Our study improves understanding of how climate change and other factors have impacted fish populations, shows the value of historical datasets for improving predictive models of population change, and can inform current and future lake management decision-making.