The stationary assumption for the traditional frequency analysis of precipitation extremes has been challenged due to natural climate variability or human intervention. To overcome this challenge, this paper, taking Heihe River basin as the case study, performed the frequency analysis by developing a nonstationary GEV model for those seasonal maximum daily precipitation (SMP) time series with nonstationary characteristics by employing the GEV conditional density estimation network. In addition, the confidence intervals (CIs) of estimated return levels were also investigated by using the residual bootstrap technique. Results showed that, 7 of 12 SMP series were nonstationary. The parameters in the nonstationary model were specified as functions of time varying or correlated climate indices varying covariates. The frequency analysis showed that the return levels varied linearly or nonlinearly with covariates. Precipitation extremes with the same magnitude in the study area were found to be occurring more frequently in the future. The CIs of such return levels increased with time passing, especially those from the more complex GEV11 model, embedding a nonlinear increasing trend in model scale parameters. It implied that the increase of model complexity is likely to result in the increase of uncertainty in estimates.

传统的极端降水频率分析的固定假设由于自然的气候变化或人为干预而受到挑战。为了克服这一挑战，本文以黑河流域为例，通过利用GEV条件密度估算网络，为那些具有非平稳特征的季节性最大日降水量（SMP）时间序列开发了一个非平稳GEV模型，进行了频率分析。此外，还使用残留自举技术研究了估计收益水平的置信区间（CI）。结果显示，在12个SMP系列中，有7个是不稳定的。非平稳模型中的参数被指定为随时间变化或相关气候指数变化的协变量的函数。频率分析表明，返回电平随协变量线性或非线性变化。在未来的研究区域中，极端极端的降水发生率会更高。此类返回水平的CI随时间流逝而增加，尤其是来自更复杂的GEV11模型的CI，并在模型比例参数中嵌入了非线性的增长趋势。这意味着模型复杂度的增加很可能导致估计的不确定性增加。在模型比例参数中嵌入非线性增长趋势。这意味着模型复杂度的增加很可能导致估计的不确定性增加。在模型比例参数中嵌入非线性增长趋势。这意味着模型复杂度的增加很可能导致估计的不确定性增加。