Although species distributions can change in an unexpectedly short period of time, most species distribution models (SDMs) use only long-term averaged environmental conditions to explain species distributions. We aimed to demonstrate the importance of incorporating antecedent environmental conditions into SDMs in comparison to long-term averaged environmental conditions. We modeled the presence/absence of 18 fish species captured across 108 sampling events along a 50-km length of the Sagami River in Japan throughout the 1990s (one to four times per site at 45 sites). We constructed and compared the two types of SDMs: (1) a conventional model that uses only long-term averaged (10-year) environmental conditions; and (2) a proposed model that incorporates environmental conditions 2 years prior to a sampling event (antecedent conditions) together with long-term averages linked to life-history stages. These models both included geomorphological, hydrological, and sampling conditions as predictors. A random forest algorithm was applied for modeling and quantifying the relative importance of the predictors. For seven species, antecedent hydrological conditions were more important than the long-term averaged hydrological conditions. Furthermore, the distributions of two species with low prevalence could not be predicted using long-term averaged hydrological conditions but only using antecedent hydrological conditions. In conclusion, incorporating antecedent environmental factors linked with life-history stages at appropriate time scales can better explain changes in species distribution through time.