It is well known that the choices of physical schemes in Regional Climate Models (RCMs) can cause considerable uncertainties in future climate projections. In this study, a factorial sensitivity analysis method has been proposed to screen out statistically significant schemes and interactions, which assists in selecting the optimized physical scheme combination from a long‐term perspective with affordable computational costs. The Regional Climate Model (RegCM) is used as an example to illustrate how the approach works. In detail, all schemes are fully tested through 120 experimental runs based on a factorial design; the contributions and statistical significance (P value) of individual schemes and their interactions to temperature, precipitation, wind speed, and wind direction are then quantified. The performance of the proposed approach is then demonstrated through a case study of Canada. The results indicate that there exist considerable spatial and temporal simulated variations associated with different scheme combinations. It is also suggested that individual physical schemes have dominant influences on simulated variations, but some effects explained by their interactions are statistically significant and thus cannot be neglected. In particular, the planetary boundary layer (PBL) scheme, moisture scheme, and land surface model are found to be the dominant factors affecting the uncertainties of temperature, precipitation, and wind speed in future climate projections over Canada, respectively. Furthermore, the potential relationships between the vegetation cover conditions and the sensitivity of physical schemes are explored. The proposed approach is an attempt to analyze the sensitivity influenced by not only individual physical schemes and also their multilevel interactions.

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The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.

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The Bayesian model averaging (BMA) method has been widely used for generating probabilistic climate projections. However, the averaging weights used in BMA can only reflect the spatially‐ and temporally‐averaged performance of each ensemble member, without the ability to address the spatiotemporal variations of model biases. This can lead to inevitable exaggeration or understatement of the contributions of individual members to the ensemble mean, thus reducing the robustness of the resulting probabilistic projections. Here we propose a new method to help address the neglected spatiotemporal variations of model biases. Through the proposed method, the BMA weights are used as prior distributions to drive the Bayesian discriminant analysis in order to generate refined weights for individual ensemble models according to their spatially‐ and temporally‐clustered performance. Through applying the proposed method to Canada, we demonstrate its effectiveness in generating robust probabilistic climate projections (e.g., the average R2 increases from 0.82 to 0.89).