The Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near-real-time seasonal global forecasts since 26 September 1997 with the NCEP global spectral model used for the reanalysis. Images of these forecasts, at daily to seasonal timescales, are provided on the World Wide Web and digital forecast products are provided on the ECPC anonymous FTP site to interested researchers. These forecasts are increasingly being used to drive regional models at the ECPC and elsewhere as well as various application models. The purpose of this paper is to describe the forecast and analysis system, various biases and errors in the forecasts, as well as the significant skill of the forecasts. Forecast near-surface meteorological parameters, including temperature, precipitation, soil moisture, relative humidity, wind speed, and a fire weather index (a nonlinear combination of temperature, wind speed, and relative humidity) are skillful at weekly to seasonal timescales over much of the United States and for many global regions. These experimental results suggest there is substantial forecast skill, out to at least a season, to be realized from current dynamical models.
*Experimental Climate Prediction Center, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California.
+Riverside Fire Laboratory, U.S. Forest Service, Riverside, California.