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Shuiqing Yin and Deliang Chen

Weather generators (WGs) are stochastic models that can generate synthetic climate time series of unlimited length and having statistical properties similar to those of observed time series for a location or an area. WGs can infill missing data, extend the length of climate time series, and generate meteorological conditions for unobserved locations. Since the 1990s WGs have become an important spatial-temporal statistical downscaling methodology and have been playing an increasingly important role in climate-change impact assessment. Although the majority of the existing WGs have focused on simulation of precipitation for a single site, more and more WGs considering correlations among multiple sites, and multiple variables, including precipitation and nonprecipitation variables such as temperature, solar radiation, wind, humidity, and cloud cover have been developed for daily and sub-daily scales. Various parametric, semi-parametric and nonparametric WGs have shown the ability to represent the mean, variance, and autocorrelation characteristics of climate variables at different scales. Two main methodologies including change factor and conditional WGs on large-scale dynamical and thermal dynamical weather states have been developed for applications under a changing climate. However, rationality and validity of assumptions underlining both methodologies need to be carefully checked before they can be used to project future climate change at local scale. Further, simulation of extreme values by the existing WGs needs to be further improved. WGs assimilating multisource observations from ground observations, reanalysis, satellite remote sensing, and weather radar for the continuous simulation of two-dimensional climate fields based on the mixed physics-based and stochastic approaches deserve further efforts. An inter-comparison project on a large ensemble of WG methods may be helpful for the improvement of WGs. Due to the applied nature of WGs, their future development also requires inputs from decision-makers and other relevant stakeholders.


Filippo Giorgi

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.