Bias correction methods are based on the assumption of bias stationarity of climate model outputs. However, this assumption may not be valid, because of the natural climate variability. This study investigates the impacts of bias nonstationarity of climate models simulated precipitation and temperature on hydrological climate change impact studies. The bias nonstationarity is determined as the range of difference in bias over multiple historical periods with no anthropogenic climate change for four different time windows. The role of bias nonstationarity in future climate change is assessed using the signal-to-noise ratio as a criterion. The results show that biases of climate models simulated monthly and annual precipitation and temperature vary with time, especially for short time windows. The bias nonstationarity of precipitation plays a great role in future precipitation change, while the role of temperature bias is not important. The bias nonstationarity of climate model outputs is amplified when driving a hydrological model for hydrological simulations. The increase in the length of time window can mitigate the impacts of bias nonstationarity for streamflow projections. Thus, a long time period is suggested to be used to calibrate a bias correction method for hydrological climate change impact studies to reduce the influence of natural climate variability.

偏差校正方法基于气候模型输出偏差平稳的假设。但是，由于自然气候的可变性，该假设可能无效。这项研究调查了模拟降水和温度的气候模型偏差非平稳性对水文气候变化影响研究的影响。偏差非平稳性被确定为在四个不同时间窗口内没有人为气候变化的多个历史时期内偏差的差异范围。以信噪比为标准评估了偏态非平稳性在未来气候变化中的作用。结果表明，模拟的气候模型偏差每月和每年的降水量和温度都随时间变化，尤其是对于短时间窗口而言。降水的偏差非平稳性在未来的降水变化中起很大作用，而温度偏差的作用并不重要。在驱动用于水文模拟的水文模型时，会放大气候模型输出的偏差非平稳性。时间窗口长度的增加可以减轻偏流非平稳性对水流投影的影响。因此，建议使用较长的时间来校准用于水文气候变化影响研究的偏差校正方法，以减少自然气候变化的影响。时间窗口长度的增加可以减轻偏流非平稳性对水流投影的影响。因此，建议使用较长的时间来校准用于水文气候变化影响研究的偏差校正方法，以减少自然气候变化的影响。时间窗口长度的增加可以减轻偏流非平稳性对水流投影的影响。因此，建议使用较长的时间来校准用于水文气候变化影响研究的偏差校正方法，以减少自然气候变化的影响。