Understanding how flood frequency changes under non-stationary hydro-climatic conditions remains a key challenge in hydrology. This study presents a Bayesian process-based framework for flood frequency analysis that explicitly accounts for the seasonal dependence of rainfall–runoff processes and their sensitivity to climate change. The approach links an event-based rainfall–runoff model with probabilistic representations of storm, soil moisture, and catchment response, allowing the joint propagation of uncertainty from climate drivers to flood quantiles. The process-based structure of the framework also enables the disentangling of individual flood drivers, such as the upward shift of the zero-degree isotherm, long-term changes in soil moisture regimes, and variations in precipitation intensity. The framework is implemented in Austrian hotspots, i.e. groups of similar catchments, using long-term hydrometeorological records and regional climate projections (EURO-CORDEX). Results show that (i) changes in flood frequency are primarily driven by projected increases in precipitation intensity, while temperature and soil moisture act as modulators or amplifiers of this signal; (ii) precipitation changes have larger but more uncertain impacts on floods than temperature and soil moisture variations; (iii) the expected reduction in soil moisture tends to mitigate frequent floods but has mores limited influence on rare events. The proposed methodology provides a transferable tool for assessing climate-sensitive flood hazards in non-stationary environments.