Accurate daily climate maps are essential for environmental monitoring, yet many regions lack dense weather-station networks. We developed a deep-learning method that converts sparse station measurements into high-resolution gridded temperature and precipitation fields over complex terrain. Tested over Romania, it outperforms traditional geostatistical interpolation by learning how topography shapes local climate, while providing reliable uncertainty estimates for each prediction.