摘要:
统计降尺度是指通过建立大尺度气候模式输出变量与小尺度气象要素之间的统计关系，产生站点或流域尺度气候变化情景的过程，是开展站点或流域尺度气候变化影响评估的重要技术环节。本文在总结统计降尺度方法国内外最新研究进展的基础上，综述了理想预报、模型输出估计以及随机天气发生器三类主要的统计降尺度方法，分析了各方法的优点与不足；阐述了统计降尺度方法发展所面临的问题与挑战，并提出针对各问题的解决思路和方法，以期为统计降尺度的发展指明方向，为气候变化影响评估提供参考。
Abstract:
Statistical downscaling is a process to build up statistical relationships between large-scale (usually 1˚-3˚ on latitude and longitude) climate model outputs and point/watershed-scale meteorological variables. It is an important technique to conduct climate change impact assessment for a specific site or a watershed. This paper systematically reviewed the recent advances in three fields related to statistical downscaling methods: perfect prognosis, model output statistics, and stochastic weather generator. Merits and draw-backs associated with each downscaling method were summarized. In addition, the challenges in pro-gressing statistical downscaling methods were stated, as well as the potential solutions. The contribution of this review is aimed at pointing out the direction of developing statistical downscaling methods and providing clues for climate change impact studies.