In this research, sparse observational data from two sources- the Chilean Dirección de Aguas and the NOAA National Climatic Data Center- were transformed into a gridded dataset using the thin-plate smoothing spline function native to MATLAB. The study focused on winter-season (May-August) precipitation over the 2007-2009 period. By analyzing trends in the gridded data, we found the following: (1) adding additional data points generally improved the interpolation, especially when these points extended the meridional or zonal extent of the analysis; and (2) intraseasonal precipitation variability in central Chile was generally low, ~6-7%, yet interannual variability was much higher, as much as 700% (May 2008 compared to May 2007). Links between precipitation amounts and tropical Pacific sea surface temperatures (ENSO) are still being examined; however, preliminary results show minimal relationship between precipitation and sea surface temperature during the short time period analyzed. Finally, it is noted that higher-resolution gridded precipitation datasets enable quick calculations of trends and anomalies, which may help governments prepare for flooding and erosion due to severe storms in addition to assisting the verification of numerical model outputs and remote sensing platforms.