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Background Whale sharks ( Rhincodon typus ), a highly migratory filter feeding species, aggregate in the coastal regions of the southern Bay of La Paz, Mexico every year. A growing eco-tourism industry is capitalising on the aggregations, yet very little is known about the drivers of seasonality. This investigation sought to determine the patterns of whale shark seasonality and identify the influence of oceanographic and atmospheric variables on the aggregations. Approach A 12-year (2003–2015) whale shark abundance data series, collected by the Whale Shark Mexico NGO, was analysed using generalised linear mixed effects models and generalised additive mixed models to investigate the influence of oceanographic and atmospheric variables on aggregation abundance. Sea surface temperature, wind, chlorophyll, and Oceanic Nino Index were used to investigate changes in monthly seasonality. In addition to these variables, moon illumination and tidal state were used to investigate fluctuation within seasons from 2012–2014. Results Aggregations of juvenile whale sharks are restricted to the southern-most region of the Bay of La Paz and occur in highest abundance between November and January. Sea surface temperature anomaly in the Bay of La Paz showed a positive effect on whale shark abundance. Oceanic Niño Index, conversely, showed a negative effect, potentially due to the disruption of the wind-driven upwelling corridor along the east coast of the Gulf of California. A thermal preference of ~24°C was seen to significantly increase daily whale shark sightings. Aggregations are thought to be associated with foraging on zooplankton blooms in the bay which may be detected by olfactory cues. Conclusions Effects of El Nino Southern Oscillation (ENSO) events have been associated with a number of changes in species distribution and abundance in the Gulf of California, and whale sharks appear to be no exception. Analysis of Gulf of California-wide oceanographic trends, matched with whale shark satellite tracking programmes, would help further improve the understanding of the drivers and timings of aggregations in the Bay of La Paz.