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ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

Climate change is known as one of the key challenges of the 21st century for sustainable agricultural development over Southwest Asia. However, not much is known about the way that climate changes might affect precipitation, temperature, and climate zones as their consequence over Southwest Asia. Here we have analysed probable changes in modified Thornthwaite climate zones by using monthly temperature and precipitation data from 320 meteorological stations during the historical period of 1971–2015, as well as the future projections of 17 GCMs (General Circulation Models) outputs under the RCP4.5 and RCP8.5 scenarios in two future periods, 2041–2070 and 2071–2100. The CMIP5 climate projections were bias corrected using the correction factors derived in the historical period and applied to simulate future climatic conditions over Southwest Asia. Results derived from multi model ensemble projections of precipitation and temperature indicated that the regional mean annual temperature by the future period of 2071–2100 is expected to increase by 2.4 and 3.8°C, under RCP4.5 and RCP8.5, respectively. Also, except for the southern and eastern parts of the Arabian Peninsula, by 2071–2100, the mean annual precipitation would significantly decrease by on average 6.2 and 7.5%, under RCP4.5 and RCP8.5, respectively. Computing climate types based on averages from three climatic periods (1971–2015, 2041–2070, and 2071–2100) showed that Southwest Asia tends to become warmer and dryer. For example, by the end of the 21st century, the percentage of the area occupied by the torrid‐arid climate type (which already covers 13.8% of the total area) would be 29.3 and 36.4% under RCP4.5 and RCP8.5, respectively. These changes are important because they would definitely have profound ecological, hydrologic, and social consequences and may affect the basic components of sustainable agricultural systems like crop and livestock productions. [ABSTRACT FROM AUTHOR]