Statistical downscaling of general circulation model outputs to evaporation, minimum temperature and maximum temperature using a key-predictand and key-station approach

A key-predictand and key-station approach was employed in downscaling general circulation model outputs to monthly evaporation, minimum temperature (T_min) and maximum temperature (T_max) at five observation stations concurrently. T_max was highly correlated (magnitudes above 0.80 at p ≤ 0.05) with evaporation and T_min at each individual station, hence T_max was identified as the key predictand. One station was selected as the key station, as T_max at that station showed high correlations with evaporation, T_min and T_max at all stations. Linear regression relationships were developed between the key predictand at the key station and evaporation, T_min and T_max at all stations using observations. A downscaling model was developed at the key station for T_max. Then, outputs of this downscaling model at the key station were introduced to the linear regression relationships to produce projections of monthly evaporation, T_min and T_max at all stations. This key-predictand and key-station approach was proved to be effective as the statistics of the predictands simulated by this approach were in close agreement with those of observations. This simple multi-station multivariate downscaling approach enabled the preservation of the cross-correlation structures of each individual predictand among the stations and also the cross-correlation structures between different predictands at individual stations.