Species Distribution Models (SDMs) are commonly used statistical tools in conservation biology, global change assessment, and reserve prioritization. Correlative SDMs relate species occurrences to environmental conditions, and it is common to model heterogeneity in the data with coarse-scale spatial and temporal predictors. However, this approach neglects the fine-scale environmental conditions experienced by most organisms. Further, most SDMs use occurrence data from short-term studies but make long-term predictions of future conditions. We compare four modeling frameworks that varied the temporal extent (short-term [1 year] versus long-term [10 years]) and resolution of environmental data (fine versus coarse). We expected that long-term data and finer temporal resolution of environmental variables would provide more accurate model predictions because they integrate variability in population sizes under varying microclimatic conditions. We built SDMs for 37 bird species in the H. J. Andrews Experimental Forest, Cascade Range, Oregon (USA). We used a 10-year (2010-2019) time series of annual observations during breeding season across 184 sites as response variables and gridded maps of hourly below forest canopy microclimate temperatures and LiDAR-derived vegetation variables as predictors. We evaluated the interannual transferability of long- versus short-term models and fine versus coarse-resolution temperature models; we also tested whether species’ functional traits affected the performance of models. Temporally dynamic (long-term) models with higher-resolution microclimate data outperformed static and short-term approaches in terms of performance (AUC difference ~ 0.10, TSS difference ~ 0.12). Model performance and similarity between spatial predictions were higher for dynamic rather than static models, especially for migratory species. Models for small bird species performed better as temporal resolution increased, whereas for long-lived species with larger body sizes, dynamic approaches performed similarly to static models. We advocate for increased use of fine-scale, long-term data in SDMs to boost the performance and reliability of future predictions under global change.