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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]

Maize production on the North China Plain (NCP) is critical to food security in China; however, currently, it is affected by climate change. Understanding the spatiotemporal distribution of the climate suitability for maize on the NCP in the present and future may help sustainably use climate‐related resources to ensure food security in China. In this study, 30 general circulation models from the Coupled Model Intercomparison Project Phase 5 and a statistical downscaling model (NWAI‐WG) were used to project meteorological data in 2021–2100 under representative concentration pathway 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations on the NCP. Based on agricultural climate suitability theory and the fuzzy mathematics method, the suitability of temperature, precipitation, and solar radiation on summer maize were analysed. The results showed that temperature suitability is decreasing for 2021–2100, especially during the stages from jointing to maturity; temperature suitability is the lowest in the southwest and increases to the northeast under both scenarios. Compared to 1996–2015, the precipitation suitability in 2021–2100 increases greatly under both scenarios, especially in the central part under RCP4.5 and in the north part under RCP8.5. Solar radiation suitability shows a decreasing trend for 1996–2015, however, an increasing trend for 2021–2100 under both scenarios. At spatial scale, the solar suitability increases from southwest to northeast. The integrated climate suitability under RCP4.5 in 2021–2100 is averaged approximately 0.8 and varies slightly, indicating climate change may do small effect on maize growth, though the high values shifting from the central part in 2021–2040 to the northern part in 2081–2100; however, under RCP8.5, the integrated climate suitability shows a downward trend, indicating that climate change will make many regions less suitable for maize growth. These results could provide basic information for agriculture to adapt to climate change and ensure food security for China. [ABSTRACT FROM AUTHOR]

Socioeconomic development and natural ecosystems of China are strongly affected by droughts. The socioeconomic risk of droughts in China should be quantified under climate change. This study assessed drought impacts on population and gross domestic product (GDP) under the 2.0°C‐warmer climate scenario, by implementing multiple general circulation model (GCM) simulations under the representative concentration pathway (RCP). Using the standardized precipitation evapotranspiration index (SPEI), we calculated drought frequencies in the base period (1986–2005) and 2.0°C‐warmer climate (2040–2059 in RCP4.5). Then, population and GDP exposures were evaluated by combining the drought frequency with population and GDP simulations under a shared socioeconomic pathway (SSP). Droughts were likely to occur more frequently under the 2°C‐warmer climate, especially for inland areas of northern China (i.e., Xinjiang, Xizang, Qinghai, Ningxia, Shaanxi, Gansu provinces), consisting mainly of mild and moderate droughts. Population exposure to drought was projected at 4.94 ± 0.36 × 109 person‐months, around 2% increase over the base period value. GDP exposure would increase significantly to 2.08 ± 0.15 × 1014 PPP $‐months, approximately 14‐fold over the base period. Probability analysis showed nearly 50% of the grid cells of China's entire territory undergoing more than an 11‐fold increase in GDP exposure. Our findings indicated the climate and GDP effects were primary contributors, respectively, to the changed population and GDP exposures to drought. Population and GDP exposures were distributed unequally across the country, having a large impact in the east where both population and economic activity are highly agglomerated. Our results reveal an urgent need to design and implement effective adaptation measures to overcome droughts, especially in eastern China. [ABSTRACT FROM AUTHOR]

The selection of general circulation models (GCMs) with high capability to represent the past and likely future climate for a specific geographical location is a crucial step to assess impacts of climate change on different sectors. This study included pool of 105 and 78 GCMs for representative concentration pathways (RCPs) 4.5 and 8.5, respectively, from Coupled Model Intercomparison Project—Phase 5, applied an advanced envelope‐based selection approach to select representative GCMs for the Koshi river basin in China and Nepal at short‐term (2016–2045), mid‐century (2036–2065) and end‐of‐century (2071–2100) periods, and developed range of possible future precipitation and temperature scenarios with high resolution downscaled data (10 × 10 km2), which is the novelty contribution of the study. Considering RCP4.5, average annual precipitation is expected to increase by 0–16%, 4–23% and 4–24% in the short‐term, mid‐century and end‐of‐century periods, respectively. Using RCP8.5, equivalent predictions are 6–20%, 6–36% and 13–49% in the short‐term, mid‐century and end‐of‐century periods, respectively. Average annual temperature is expected to increase, but with higher increases during winter than in the monsoon period. Considering RCP4.5, average annual temperature is expected to increase by 1–1.4°C, 1.3–1.9°C and 1.6–2.8°C in the short‐term, mid‐century and end‐of‐century periods, respectively. Similarly, using RCP8.5, equivalent predictions are 1–1.6°C, 1.8–2.9°C and 3.1–5.6°C in the short‐term, mid‐century and end‐of‐century periods, respectively. The ensemble mean of absolute change in average precipitation and temperature projects that High Himalaya and Tibet regions are more sensitive to climate change considering precipitation and temperature, respectively. The results also suggest that GCMs selection for a catchment varies with climate scenarios and specific future time periods. [ABSTRACT FROM AUTHOR]