High-resolution gridded meteorological data are essential for hydrological impact studies, yet their reconstruction from sparse station networks remains challenging. We introduce Spatial Pattern Regression (SPR), a data-driven method that reconstructs gridded meteorological fields by combining spatial information extracted from high-resolution regional climate model (RCM) simulations with station observations. SPR operates in two steps: spatial patterns are first extracted from RCM data using principal component analysis, then daily fields are reconstructed through linear regression using available observations. The method is first evaluated using controlled synthetic experiments, where virtual stations selected as a subset of the RCM grid emulate observational networks with varying density, size, and location. SPR is then validated using real station observations. Daily precipitation, minimum temperature, and maximum temperature are considered. Results show that SPR performs better than inverse distance weighting, ordinary kriging, and kriging with external drift, particularly under sparse network conditions. Sensitivity analyses highlight the dominant role of station density and location on interpolation accuracy, supporting the robustness and applicability of SPR for hydrological studies.