This study examined four univariate kriging techniques; simple kriging (SK), ordinary kriging (OK), multi-Gaussian kriging (MGC), and log-normal kriging (LNK); and two multivariate kriging algorithms; kriging with external drift (KED) using elevation and slope in two different models for the estimation of daily rainfall in a 250 m × 250 m grid over a 750 km2 area in the Canadian Boreal forest. Multivariate kriging did not enhance daily rainfall predictions. SK, OK, and LNK produced statistically comparative results with OK being slightly better. MGC was the worst univariate estimator, mainly due to the high percentage of data spikes. Sequential Gaussian simulation (SGS) was then implemented to produce 100 equiprobable maps of rainfall. A multi-objective approach; that is based on overlaying the map of the kriging variance, the DEM, and land use/land cover maps in a GIS framework to identify the areas of commonly favourable features; was proposed to identify potential future sampling locations.