This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2ĝ€m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden-Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.
All Science Journal Classification (ASJC) codes
- Water Science and Technology
- Earth and Planetary Sciences (miscellaneous)