Downscaling is an effective technique to bridge the gap between climate model outputs and data requirements of most crop and hydrologic models for assessing local and site-specific climate change impacts, especially on future food security. However, downscaling of temporal sequences, extremes in daily precipitation, and handling of nonstationary precipitation in future conditions are considered common challenges for most statistical downscaling methods. This study reviewed the above key challenges in statistical downscaling and proposed potential solutions. Ten weather stations located across the globe were used as proof of concept. The use of a stochastic Markov chain to generate daily precipitation occurrences is an effective approach to simulate the temporal sequence of precipitation. Also, the downscaling of precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Nonstationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this study are of great significance in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.

缩减规模是弥合气候模型输出与大多数作物和水文模型的数据需求之间的差距的有效技术，用于评估当地和特定地点的气候变化影响，尤其是对未来粮食安全的影响。但是，时间序列的缩减，每日降水的极端值以及未来条件下的非平稳降水的处理被认为是大多数统计缩减方法的普遍挑战。这项研究回顾了统计缩减规模中的上述主要挑战，并提出了潜在的解决方案。全球十个气象站被用作概念证明。利用随机马尔可夫链产生每日降水量是模拟降水时间序列的有效方法。还，极端降水的缩减可以通过调整概率分布的偏度系数来实现，因为它们之间具有高度相关性。可以通过根据气候模型预测的未来降水变化信号调整概率分布的参数来解决降水降尺度的非平稳性。这项研究提出的观点对于利用气候模型的输出评估当地和特定地点的气候变化影响，特别是对未来粮食安全的影响，具有重要意义。