As the frequency of extreme temperature events increases, so does the need for robust tools to understand them. This work develops and applies a methodological framework to model the occurrence of T<sub>x</sub> calendar-day records and their relationship with geopotentials. The analysis includes T<sub>x</sub> data from 36 Spanish stations (1960–2023) and geopotentials at 300, 500, and 700 hPa. Exploratory analysis revealed a non-stationary trend in records, a higher frequency in the interior of the peninsula, and decreasing spatial co-occurrence with distance. A hierarchical spatio-temporal logistic regression algorithm prioritizing interpretability was designed. The approach involves: (1) fitting local models per station; (2) applying a spatial consensus filter to reduce initial 1620 parameters to 17 in a base model; and (3) incorporation of interaction terms. Among the tested models, a global model that enhances the base model with geodetic interactions was selected for optimal balance between predictive performance and complexity. Geopotentials at 700 hPa are most relevant for characterizing records, while 300 hPa dominates in the southern corners and 500 hPa in the northern corners. The model demonstrates high predictive accuracy at interior stations, good performance at coastal stations, and adequately reproduces the persistence of record runs and spatial co-occurrence.