Climgen
http://ccafs-climate.org/statistical_downscaling_climgen/
ClimGen is based on the so-called "pattern-scaling" approach to generating spatial climate change information for a given global-mean temperature change. The pattern-scaling approach relies on the assumption that the pattern of climate change (encompassing the geographical, seasonal and multi-variable structure) simulated by coupled atmosphere-ocean general circulation models (AOGCMs) is relatively constant (for a given AOGCM) under a range of rates and amounts of global warming, provided that the changes are expressed as change per unit Kelvin of global-mean temperature change. These normalised patterns of climate change do, however, show considerable variation between different AOGCMs, and it is this variation that ClimGen is principally designed to explore. Further scientific details are provided in the technical paper given below.
- CLIMGEN was used for A1B emission scenario. 7个GCMs
- 2006-2100时间序列，0.5°分辨率。
WorldClim Version 1
http://worldclim.com/cmip5_30s
- 基于历史气候校准过的最高到30″（大约900m）分辨率未来气候数据集，20个CMIP5模式。
- 非时间序列，未来使用两个代表年份。Time periods: 2050 (average for 2041-2060) and 2070 (average for 2061-2080)
MarkSimGCM
http://gisweb.ciat.cgiar.org/MarkSimGCM/#tabs-2
- 针对CMIP5 17个全球气候模式，可以生成任一位点的2010-2095年逐日气候（辐射，最高，最低温度，降雨）数据。
- 由于主要针对发展中国家，观测位点较少甚至无观测数据地区，不用增加自己的观测数据。
- MarkSim itself contains a calibration dataset of about 10,000 stations worldwide most of which have 15-20 years of historical daily data.
- 输出可下载的文本文件，及条形图和polar图。
KNN-WG
https://agrimetsoft.com/knn-wg
- KNN天气生成器是一种基于k -近邻方法的日天气数据模拟工具。用户可以加载7个不同的变量，Tmin、Tmax、Rain、Srad、ETo、风速和湿度。然后，用户可以加载输入数据并运行KNN天气发生器。在该软件中，用户可以输出图形以及获得模拟结果的评价：d, NSE, RMSE, MBE, Pearson和Spearman。用户可以将KNN天气发生器的输出与Lars-WG、SDSM、CMIP5未来气候降尺度产品进行比较。
- 与时间序列生成的参数选择不同，非参数方法通过使用概率原理有条件地重新采样过去的观测值来生成新值。而传统的天气发生器通过建立观测数据与GCM的关系来完成降尺度。（后面再学习。。。）
SDSM
