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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.


Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation planning, downscaling approaches have been developed. Regional climate change modeling, even though it is still a matter of basic research and questioned by many researchers, is urged to provide operational results. One major downscaling class is statistical downscaling, which exploits empirical relationships between larger-scale and local weather. The main statistical downscaling approaches are perfect prog (often referred to as empirical statistical downscaling), model output statistics (which is typically some sort of bias correction), and weather generators. Statistical downscaling complements or adds to dynamical downscaling and is useful to generate user-tailored local-scale information, or to efficiently generate regional scale information about mean climatic changes from large global climate model ensembles. Further research is needed to assess to what extent the assumptions underlying statistical downscaling are met in typical applications, and to develop new methods for generating spatially coherent projections, and for including process-understanding in bias correction. The increasing resolution of global climate models will improve the representation of downscaling predictors and will, therefore, make downscaling an even more feasible approach that will still be required to tailor information for users.