A comparison of two downscaling methods for precipitation in China

In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Pre(sim1) and Pre(sim2), using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Pre(sim2), which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Pre(sim1) is much better than Pre(sim2) based on mean absolute error, mean relative error and root mean square error. Pre(sim1) fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification.