摘要：We present historical monthly spatial models of temperature and precipitation generated from the North American dataset version 鈥渏鈥?from the National Oceanic and Atmospheric Administration鈥檚 (NOAA鈥檚) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901鈥?016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94鈥壜癈 for maximum temperature models, 1.3鈥壜癈 for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01鈥壜癈, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5鈥塵m or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7鈥壜癈 for maximum temperature, 1.02鈥壜癈 for minimum temperature, and 13.3鈥塵m (19.3% of the surface mean) for monthly precipitation.
