Estimation of gridded precipitation is a major point of interest in climatological and hydrological research. Using a novel approach based around kernel density estimation we attempt to improve on a currently available estimators of gridded precipitation in both accuracy and under- standing uncertainty in predictions by generating robust precipitation climatologies. The method is constructed and validated using the United States Historical Climatology Network dataset covering the continental United States with sparse and irregular observation stations and accurate probability distributions that capture seasonal variance in the data are generated. Spatial estimates of local climatologies at arbitrary locations, both in and out of the observational network, are analyzed and an accurate method using generalized additive models is developed. Finally a preliminary analysis of gridded estimation is discussed and serves as a motivation for further research.