The use of daily climate data in agriculture has increased considerably over the past two decades due to the rapid development of information technology and the need to better assess impacts and risks from extreme weather and accelerating climate change. While daily station data is now regularly used as an input to biophysical and biogeochemical models for the study of climate, agriculture, and forestry, questions still remain on the level of uncertainty in using daily data, especially for predictions made by spatial interpolation models. We evaluate the precision of three models (i.e., spline, weighted-truncated Gaussian filter, and hybrid inverse-distance/natural-neighbor) for interpolating daily precipitation and temperature at 10 km across the Canadian landmass south of 60o latitude (encompassing Canada's agricultural region). We compute daily, weekly, and monthly-aggregated bias and root-mean-square (RMSE) validation statistics, examining how error varies with orography and topography, and proximity to large water. Our findings show the best approach for interpolating daily temperature and precipitation across Canada requires a mixed-model/Bayesian approach. Further application of interpolation methods that consider non-stationary spatial covariance, alongside measurement of spatial correlation range would aid considerably in reducing interpolation prediction uncertainty. Copyright © 2010 Crown in Right of Canada.