Abstract
Researchers are aware of certain types of problems that arise when modelling interconnections between general circulation and regional processes, such as prediction of regional, local‐scale climate variables from large‐scale processes, e.g. by means of general circulation model (GCM) outputs. The problem solution is called downscaling. In this paper, a statistical downscaling approach to monthly total precipitation over Turkey, which is an integral part of system identification for analysis of local‐scale climate variables, is investigated. Based on perfect prognosis, a new computationally effective working method is introduced by the proper predictors selected from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data sets, which are simulated as perfectly as possible by GCMs during the period of 1961–98. The Sampson correlation ratio is used to determine the relationships between the monthly total precipitation series and the set of large‐scale processes (namely 500 hPa geopotential heights, 700 hPa geopotential heights, sea‐level pressures, 500 hPa vertical pressure velocities and 500–1000 hPa geopotential thicknesses). In the study, statistical preprocessing is implemented by independent component analysis rather than principal component analysis or principal factor analysis. The proposed downscaling method originates from a recurrent neural network model of Jordan that uses not only large‐scale predictors, but also the previous states of the relevant local‐scale variables. Finally, some possible improvements and suggestions for further study are mentioned. Copyright © 2004 Royal Meteorological Society