A nonstationary bias-correction technique to remove bias in GCM simulations

We developed an updated nonstationary bias-correction method for a monthly global climate model of temperature and precipitation. The proposed method combines two widely used quantile mapping bias-correction methods to eliminate potential illogical values of the variable. Instead of empirical parameter estimation in the more-common quantile mapping method, our study compared bias-correction performance when parametric or nonparametric procedures were used to estimate the probability distribution. The results showed our proposed bias-correction method to be very effective in reducing the model bias: it removed over 80% and 83% of model bias for surface air temperature and precipitation, respectively, during the validation period. Compared with a widely used method of bias correction (delta change), our proposed technique demonstrates improved correction of the distribution of variables. In addition, nonparametric estimation procedures further reduced the mean absolute errors in temperature and precipitation during the validation period by approximately 2% and 0.4%, respectively, compared with parametric procedures. The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively, at the global land scale.