This study investigates the skill of linear methods for downscaling provincial-scale precipitation over Indonesia from fields that describe the large-scale circulation and hydrological cycle. The study is motivated by the strong link between large-scale variations in the monsoon and the El Nino - Southern Oscillation (ENSO) phenomenon and regional precipitation, and the subsequent impact of regional precipitation on rice production in Indonesia. Three different downscaling methods are tested across five different combinations of large-scale predictor fields, and two different estimates of regional precipitation for Indonesia.
Downscaling techniques are most skillful over the southern islands (Java and Bali) during the monsoon onset or transition season (Sep.-Dec.). The methods are moderately skillful in the southern islands during the dry season (May-Aug.), and exhibit poor skill during the wet season (Jan.-Apr.). In northern Sumatra downscaling methods are most skillful during Jan.-Apr. with little skill at other times of the year. There is little difference between the three different linear methods used to downscale precipitation over Indonesia. Additional analysis indicates that downscaling methods that are trained on the annual cycle of precipitation produce less-biased estimates of the annual cycle of regional precipitation than raw model output, and also show some skill at reconstructing interannual variations in regional precipitation. Most of the downscaling methods' skill is attributed to year-to-year ENSO variations and to the long-term trend in precipitation and large-scale fields.
While the goal of the present study is to investigate the skill of downscaling methods specifically for Indonesia, results are expected to be more generally applicable. In particular, the downscaling models derived from observations have been effectively used to debias the annual cycle of regional precipitation from global climate models. It is expected that the methods will be generally applicable in other regions where regional precipitation is strongly affected by the large-scale circulation.