Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.

需要降尺度技术来描述粗网格分辨率的全球气候模型输出与相关更精细尺度的地表水文变量之间的联系，以进行气候变化影响和适应研究。特别是，在许多先前的研究中已经提出了几种统计方法，用于缩小单个本地站点的极端温度序列，而没有考虑到这些序列在不同位置之间观察到的空间依赖性。因此，本研究提出了一种改进的统计方法来缩小每日最大值 ( Tmax ) 和最小值 ( Tmin) 同时位于许多不同地点的温度系列。这种新的多站点多元统计降尺度（MMSD）方法是基于通过多元线性回归模型对当地每日极端温度和全球气候预测因子之间的联系进行建模的组合；并通过组合奇异值分解和多元自回归（SVD-MAR）模型对其随机分量进行建模，以更有效和更准确地表示这些极端日温度序列的时空变化。使用来自孟加拉国四个气象站网络的每日极端温度数据和两个不同的 NCEP/NCAR 再分析数据集的说明性应用结果表明了所提出方法的有效性和准确性。尤其是，单个站点的Tmax和Tmin以及不同位置之间极端温度的空间变异性。此外，已经证明，所提出的方法可以产生比广泛使用的单站点缩减 SDSM 程序所给出的结果更好的结果，特别是在保留观察到的站点间相关性方面。