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The effectiveness and efficiency of two state‐of‐the‐art global sensitivity analysis (SA) methods, the Morris and surrogate‐based Sobol' methods, are evaluated using the Weather Research and Forecasting (WRF) model, version 3.6.1. The sensitivities of precipitation and other related meteorological variables to 11 selected parameters in the new Kain‐Fritsch Scheme, WRF Single‐Moment 6‐class Scheme, and Yonsei University Scheme are then investigated. The results demonstrate that (1) the Morris method is effective and efficient for screening important parameters qualitatively, and with recommended settings of levels p = 8 and replication times r = 10 only 10 × (D + 1) WRF runs are required, where D is the dimension of parameter space; (2) Gaussian process regression (GP) is the best method for constructing surrogates, and the GP‐based Sobol' method can provide reliable quantitative results for sensitivity analysis when the number of WRF runs exceeds 200; and (3) the sensitivity index μ∗ in the Morris method is closely related to the Sobol' index ST, and even for qualitative sensitivity analysis, the GP‐based Sobol' method is more efficient compared to the Morris method. The SA results show that larger values of the downdraft‐related parameter x1, entrainment‐related parameter x2, and downdraft starting height x3 significantly decrease rainfall, while the maximum allowed value for the cloud ice diameter x6 has a moderate decreasing effect on precipitation. This work is useful for further tuning of the WRF to improve the agreement between the climate model and observations.

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As a typical arid and semi-arid area, central Asia (CA) has scarce water resources and fragile ecosystems that are particularly sensitive and vulnerable to climate change. In this study, dynamic downscaling was conducted to produce a regional dataset that incorporated the time period 1986-2100 for the CA. The results show that dynamic downscaling significantly improves the simulation for the mean and extreme climate over the CA, compared to the driving CCSM4 model. We show that significant warming will occur over CA with 2.0 °C and 5.0 °C increasing under the RCP4.5 and RCP8.5 scenarios, respectively by the end of twenty-first century. The daily maximum temperature, the daily minimum temperature and the annual total number of days with a minimum temperature greater than 25 °C will also increase significantly. The annual total number of days with a minimum temperature less than 0 °C will decrease significantly. Long-term trends in the projected winter precipitation under different emission scenarios exhibit robust and increasing changes during the twenty-first century, especially under the RCP8.5 scenario with an increasing about 0.1 mm/day. Significant differences are shown in the projection of precipitationrelated indices over CA under different emission scenarios, and the impact of emissions is apparent for the number of days with ≥ 10 mm of precipitation, the density of precipitation on days with ≥ 1 mm of precipitation, and particularly for the maximum consecutive number of dry days that will increase significantly under the RCP8.5 scenario. Therefore, reduced greenhouse gases emissions have implications for mitigating extreme drought events over the CA in the future.