Over recent decades, high temperatures have intensified worldwide, with impacts on human health, ecosystems, and the economy. Understanding their spatial evolution requires accounting for long-term temperature changes and the role of atmospheric circulation. Quantile regression provides a flexible framework for characterizing changes across the temperature distribution, with relevance to extremes. We analyze the spatio-temporal evolution of the 0.50 and 0.95 quantiles of daily maximum temperature during summer (June--August) over peninsular Spain from 1960 to 2023. We develop a Bayesian spatially varying quantile regression model for observations from 40 meteorological stations, using ERA5 geopotential fields at tropospheric levels to characterize atmospheric circulation, together with long-term temporal trends capturing the background climate change signal. The spatially varying coefficients allow the sensitivity of temperature to atmospheric circulation to differ across the region, linking synoptic dynamics with local temperature variability. The model separates changes associated with the dynamical atmospheric state from the long-term thermodynamic warming signal, while allowing their effects to vary across the temperature distribution. Results show that atmospheric circulation and long-term trends provide comparable contributions to the observed warming. Significant increases in median and upper-quantile temperatures are found across much of peninsular Spain, with pronounced heterogeneity across months and space, followed by quantiles

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