In this work, the authors analyze the observed long-term variations of seasonal climate in China and then investigate the possible influence of increases in greenhouse gas concentrations on these variations by comparing the observations with the simulations of the second phase of the Coupled Model Intercomparison Project (CMIP2). The long-term variations of precipitation and temperature in China are highly seasonally dependent. The main characteristic of summer precipitation in China is a drying trend in the north and a wetting trend in the central part. The precipitation in winter shows an increasing trend in southern and eastern-central China. Interesting features have also been found in the transitional seasons. In spring, precipitation variations are almost opposite to those in summer. In autumn the precipitation decreases in almost the whole country except for the middle and lower reaches of the Yangtze River Valley. In addition, the seasonality of precipitation has become slightly weaker in recent decades in southern and eastern China. Pronounced warming is observed in the entire country in winter, spring, and autumn, particularly in the northern part of China. In summer a cooling trend in central China is particularly interesting, and cooling (warming) trends generally coexist with wetting (drying) trends. The correlativity between precipitation and temperature variations is weak in spring, autumn, and winter. It has also been found that the long-term climate variations in winter and summer in China may be connected to the warming trend in the sea surface temperature of the Indian Ocean. A comparison between the observed seasonal climate variations and the CMIP2 simulations of 16 models indicates that the observed long-term variations of winter, spring, and autumn temperature in China may be associated with increases in greenhouse gas concentrations. However, such a connection is not found for the summer temperature. The tremendous uncertainties among the models in precipitation simulations make it difficult to link the precipitation variations to global warming.