The importance of modelling temperature fields goes beyond the need to understand a region's climate and serves too as a starting point for understanding their socioeconomic, and health consequences. The topography of the study region contributes much to the complexity of modelling these fields and demands flexible spatio-temporal models that are able to handle nonstationarity and changes in trend. In this paper, we develop a flexible stochastic spatio-temporal model for daily temperatures in the Pacific Northwest, and describe a methodology for performing Bayesian spatial prediction. A novel aspect of this model, an extension of the spatio-temporal model proposed in Le and Zidek (1992), is its incorporation of site-specific features of a spatio-temporal field in its spatio-temporal mean. Due to the often surprising Pacific Northwestern weather, the analysis reported in the paper shows the need to incorporate spatio-temporal interactions in that mean in order to understand the rapid changes in temperature observed in nearby locations and to get approximately stationary residuals for higher level analysis. No structure is assumed for the spatial covariance matrix of these residuals, thus allowing the model to capture any nonstationary spatial structures remaining in those residuals.