LAUSR

The use of different downscaling methods (DSMs) to generate downscaled daily climate (DDC) data for assessing climate change impacts on wheat cropping systems was investigated. DDC data were generated from SRES A2 emission scenario simulations of seven global climate models (GCMs) using two different change factor methods, denoted as DTS and RSC, and two weather generator methods, LARS-WG (LWG) and NWAI-WG (NWG). The DDC data were used to drive the Agricultural Production Systems sIMulator (APSIM) wheat model. Significant differences in future changes in simulated crop growth and soil water balance were found between the four DSMs. For raw GCM output, multi-GCM mean changes by the mid-twenty-first century in annual mean temperature (AMT) ranged from +1.2 to +2.0 °C and in annual rainfall (AR) ranged from −11 to −5% across the 6 study sites. The DTS, LWG and NWG SDMs modified the raw GCM changes in both AMT and AR. As a result, climate change impacts on crop growth and soil water balance are not well correlated between DSMs. Since different DSMs give different impact results, we conclude that in addition to using multiple GCMs, selecting appropriate DSMs can be an important consideration in climate change impact assessments.