Background
The authors write that "testing models' ability to reproduce 'present climate' and past climate changes is an important part of evaluating the GCM projections," citing Phillips and Gleckler (2006), Randall et al. (2007), Walsh et al. (2008) and Mote and Salathe (2010). In fact, one could truthfully say that such testing is essential.

What was done
As Su et al. describe it, "the performance of 24 GCMs available in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) was evaluated over the eastern Tibetan Plateau (TP) by comparing the model outputs with ground observations for the period 1961-2005," focusing their attention on both temperature and precipitation.

What was learned
The five researchers report that with respect to temperature, "most GCMs reasonably capture the climatological patterns and spatial variations of the observed climate," but they say that "the majority of the models have cold biases, with a mean underestimation of 1.1°-2.5°C for the months December-May, and less than 1°C for June-October." As for precipitation, they state that "the simulations of all models overestimate the observations in climatological annual means by 62.0%-183.0%," while noting that "only half of the 24 GCMs are able to reproduce the observed seasonal pattern," including "the sharp contrast between dry winters and wet summers."

What it means
The last of these observations clearly suggest, as Su et al. note, that there is "a critical need to improve precipitation-related processes in these models." And the fact that they found 90-year forward projections of both precipitation and temperature to "differ much more among various models than among emissions scenarios" suggests that temperature-related processes have a critical need to be improved upon as well.

References
Mote, P.W. and Salathe, E.P. 2010. Future climate in the Pacific Northwest. Climatic Change 109: 29-50.

Phillips, T.J. and Gleckler, P.J. 2006. Evaluation of continental precipitation in 20th century climate simulations: The utility of multi-model statistics. Water Resources Research 42: 10.1029/2005WR004313.

Randall, D.A. et al. 2007. Climate models and their evaluation. In: Solomon, S., Qin, D., Manning, M., Marquis, M., Averyt, K., Tignor, M.B., Miller Jr., H.L. and Chen, Z. (Eds.). 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, Cambridge, United Kingdom, pp. 589-662.

Walsh, J.E., Chapman, W.L., Romanovsky, V., Christensen, J.J. and Stendel, M. 2008. Global climate model performance over Alaska and Greenland. Journal of Climate 21: 6156-6174.