Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have observation biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, which data source is more useful for phenological analyses is unclear. We use parallel analyses of incidental and survey data to determine how traits and climate drive phenological patterns for common butterflies. One workflow aggregated “Pollard” surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 routinely observed resident species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. Results were similar between datasets. Inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species that overwinter at more advanced stages. Other traits had mixed or no impacts. The consistency in results suggests that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.