Global Climate Models (GCMs) offer valuable seasonal precipitation forecasts information. However, their predictive performance may be inferior to traditional climatological forecasts derived from historical precipitation data. In this study, we evaluate the spatiotemporal skill of calibrated GCMs across China to determine whether their skill surpasses climatological forecasts at various lead times. Six GCMs are statistically calibrated using a Gamma-Gaussian model and integrated via Bayesian Model Averaging. The calibrated GCMs forecast is then compared with the climate forecast for different climate zones and months, and in the formulation of actual meteorological business and academic research, integer months are often used to describe the forecast period, and the preparation time is 1 month, 2 months, and 3 months. The results indicate that for the one-month lead time, the skill of calibrated GCM forecasts outperforms climatological forecasts in 33% (322/971) of grid cells. However, the skill of calibrated GCMs declines with longer lead times, with only 24% and 20% of grid cells surpassing the climatological forecasts at two- and three-month lead times, respectively. Regionally, the calibrated GCMs forecasts exhibit stronger superiority over climatological forecasts in the Northern Subtropical Zone than in other climate zones, while showing the most limited improvement compared to climatological benchmarks in the Middle Temperate Zone. Seasonally, the skill advantages of the calibrated forecasts relative to climatological forecasts are more pronounced during the non-flood season (September to March) than during the flood season (April to August). The average proportions of grid cells during the flood season are 29%, 18%, and 18% across the three lead times, compared to 49%, 34%, and 27% during the non-flood season. Overall, this study provides a comprehensive evaluation of the skill of calibrated GCMs across China, offering a framework for assessing their effectiveness in delivering reliable seasonal precipitation forecasts in other regions.
Yu et al. (Mon,) studied this question.