Human mobility played a key role in shaping the spatiotemporal dynamics of COVID19 transmission. This study employs Transfer Entropy (TE), an information-theoretic approach, to investigate the directional relationship between interregional mobility and COVID19 spread in Spain. Specifically, we use the mobility-associated risk time series, derived from phone-based origin-destination data and local infection prevalence, to estimate the flow of potentially infected individuals between regions. TE is then applied to measure the information flow from mobility-associated risk to regional case counts, enabling us to uncover spatio-temporal patterns of mobility-driven transmission. Using real-world data, we identified provinces that acted as outbreak drivers during the COVID19 pandemic in Spain and detected temporal shifts in the strength and direction of mobility's influence. Our findings align with key epidemiological events, such as the 2020 summer outbreak in Lleida linked to seasonal workers, and highlight the effects of non-pharmaceutical interventions, including bar closures in Catalunya, on transmission dynamics. Finally, we validated our approach using simulations from a metapopulation SIR model with known transmission pathways, showing that TE can recover mobility-induced transmission structure while reducing indirect or spurious associations. Altogether, our work provides a novel approach to study the effect of interregional mobility on epidemic spread and to uncover spatio-temporal patterns of mobility-driven transmission, offering valuable insights to inform the timing and regional targeting of non-pharmaceutical interventions.