High-resolution meteorological data are necessary to understand and predict climate-driven
impacts on the structure and function of terrestrial ecosystems. However, the spatial …

The increasing demand for high-resolution climate information has attracted growing
attention to statistical downscaling (SDS) methods, due in part to their relative advantages …

To make sound decisions in the face of climate change, government agencies, policymakers
and private stakeholders require suitable climate information on local to regional scales. In …

Credible information about the properties and changes of extreme events on the regional
and local scales is of prime importance in the context of future climate change. Within the EU …

VALUE is a network that developed a framework to evaluate statistical downscaling methods
including model output statistics such as simple bias correction and quantile mapping; …

Downscaling is a widely used technique for translating information from large-scale climate
models to the spatial and temporal scales needed to assess local and regional climate …

Urban systems are not only major drivers of climate change but also the impact hotspots. In
the context of the planet currently undergoing a process of greenhouse warming, and …

This paper highlights the importance of improving stakeholder understanding of
environmental factors such as air quality and the urban heat island effect to achieve …

Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-
corrected climate information, making it important to understand the uncertainties and …

We present an unprecedented ensemble of 196 future climate projections arising from
different global and regional model intercomparison projects (MIPs): CMIP3, CMIP5 …