High resolution spatially extended values of climate variables play a central role in the assessment of climate and projected future climate in ecosystem modelling. The ground based meteorological network remains a key resource for deriving these spatially extended climate variables. Thin plate smoothing splines, as implemented in the ANUSPLIN package, have been used widely in these derivations. The method is able to robustly calibrate topographic dependence of climate variables in remote data sparse areas. We report on the production, and applications, of new anomaly based fine scale spatial interpolations of the key climate variables at daily and monthly time scale, across the Australian continent. The methods incorporate several innovations that have

significantly improved spatial predictive accuracy. The accuracy, and robustness to data error, of anomaly-based interpolation has been enhanced by incorporating physical process aspects of the different climate variables. New regression procedures have also been developed to estimate “background” monthly climate normals from all stations with minimal records to substantially increase the density of supporting spatial networks. Monthly mean temperature interpolation has been enhanced by incorporating process-based coastal effects that have

significantly reduced predictive error. Overall errors in interpolated daily and monthly climate fields are significantly less than those reported by earlier studies. For monthly and daily precipitation, a new anomaly structure has been devised to take account of the skewness in precipitation data and the large proportion of zero values. These issues present significant challenges to standard interpolation methods. Coupled with the anomaly interpolation process are practical innovations to permit systematic processing of many data sets and semi-automated use of studentized residuals to reliably remove data outliers.