In China, efforts to predict the climate started in the 1930s. Experimental operational climate outlooks have been carried out since the late 1950s, based on historical analog circulation patterns. However, only from late 1980s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program (WCRP) was launched. This presentation aims to provide an overview of studies on the seasonal climate prediction in China. Many processes and factors are associated with the climate variability and predictability, i.e., the tropical sea surface temperature anomalies in the Pacific, Indian, and Atlantic. Various land surface processes have been found to be associated with the climate of China, particularly the soil moisture and the snow cover over the Tibetan Plateau and Eurasia. Chinese scientists have studied various teleconnection patterns that relate to the climate of China, including the well-known ‘Pacific-Japan Pattern' or ‘East Asia-Pacific Pattern' and the North Atlantic oscillation (NAO) as well as the Antarctic oscillation (AAO). These remote patterns have been shown to be important factors in modulating the climate of China in a number of papers. The GCM-based seasonal climate prediction experiment was carried out in 1989 in China. Many such experiments have been performed since then by using different models for multiple years, both retrospectively and in real time. All of these models show promising but constrained forecast skills for precipitation and temperature predictions. Therefore, much effort has been dedicated to developing new techniques to improve seasonal predictions. These endeavors involve several aspects including dynamical and statistical downscaling, developing new schemes that use year-to-year increments as the new predictand instead of the anomalies, and developing approaches to make adequate use of tropical predictability in reinforcing extra-tropical predictability. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, like the heavy frozen rain event in January 2008 in South China.

Key words: seasonal prediction, climate variability, prediction