Spatial Pattern Regression (SPR) is a new way to reconstruct daily weather fields in regions with few measurement stations. Our approach combines information from past high-resolution simulations with available observations to produce more accurate maps of precipitations and temperature. Tests on both synthetic and real data show clear improvements over common methods, especially when stations are sparse, helping support better hydrological and climate studies.