Climate change has been recognized as having a profound impact on the hydrologic cycle, and Global Climate Models (GCMs) have been extensively used in many studies for assessing this impact.However, outputs from these models are usually at too coarse resolutions and thus not suitable for hydrological impact assessments at a regional or local scale.Different downscaling methods have been hence proposed for linking GCM predictions of climate change to hydrologic processes at the relevant space and time scales for these impact studies.If this linkage could be established, then the GCM projected change of climate conditions could be used to predict the resulting changes of local hydrologic variables.Therefore, the overall objective of this presentation is to provide an overview of some recent progress in the modeling of extreme precipitations and temperatures in a changing climate.In particular, the main focus is on recently developed statistical downscaling (SD) methods for linking GCM climate predictors to the observed precipitation and temperature extremes at a single site as well as at many sites concurrently.Many previous works have been dealing with downscaling of these processes at a single site, but very few studies are concerned with the downscaling of these series for many locations because of the complexity in describing accurately both observed at-site temporal persistence and observed spatial dependence between different sites.In addition, new SD procedures are presented for describing the linkages between GCM outputs and precipitation characteristics at a given location where the precipitation data are limited or unavailable, a common and crucial challenge for water resources planning and management in practice.Examples of various applications using data from different climatic conditions in Canada and in other countries in Asia will be presented to illustrate the feasibility and accuracy of the proposed methods.