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The existence and development of oases in arid plain areas depends mainly on the runoff generated from alpine regions. Quantifying the uncertainties of runoff simulation under climatic change is crucial for better utilization of water resources and management of oases in arid areas. In the present study, based on the ungauged Qira River Basin in Xinjiang, China, a modified version of the Delta statistical downscaling method was applied to reconstruct the monthly mean temperature (MMT), monthly accumulated precipitation (MAP), and monthly accumulated evaporation (MAE) of two target stations. Then, the uncertainty in runoff simulation, implemented using the Three-Layered Feedforward Neural Network model with the Back-Propagation learning algorithm, was quantified. The modified Delta method reproduced the MMT, MAP, and MAE time series of the two target stations very well during the calibrated periods, and the reconstructed uncertainty ranges were small among reconstructed datasets using data from 12 observation stations. The monthly accumulated runoff simulated by the reconstructed MMT, MAP, and MAE as input variables of the model possessed unpredictable uncertainty. Although the use of multi-data ensembles in model inputs are considered an effective way to minimize uncertainties, it could be concluded that, in this case, the efficiency of such an approach was limited because of errors in the meteorological data and the deficiency of the model’s structure. The uncertainty range in the runoff peak was unable to capture the actual monthly runoff. Nevertheless, this study represents a significant attempt to reproduce historical meteorological data and to evaluate the uncertainties in runoff simulation through multiple input ensembles in an ungauged basin. It can be used as reference meteorological data for researching long-term climate change and producing runoff forecasts for assessing the risk of droughts and/or floods, as well as the existence and management of plain oases in the Qira River Basin.