Editing IPCC:AR6/WGI/Chapter-10 (section)

===== 10.3.1.3.4 Weather generators =====

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Weather generators are statistical models that simulate weather time series of arbitrary length. They are calibrated to represent observed weather statistics, in particular daily or even sub-daily variability. One variant of these models are advanced stochastic perfect-prognosis methods, conditioned on large-scale atmospheric predictors on a daily basis, for instance multisite generalized linear models ( [[#Chandler--2020|Chandler, 2020]] ). Another widely used variant is change-factor weather generators: the weather generator parameters are calibrated against present and future climate model simulations, and the climate change signals are then applied to the parameters calibrated to observations. Recent research has mainly focussed on multi-site Richardson type (Markov-chain) weather generators ( [[#Keller--2015|Keller et al., 2015]] ; [[#Dubrovsky--2019|Dubrovsky et al., 2019]] ), some explicitly modelling extremes and their spatial dependence ( [[#Evin--2018|Evin et al., 2018]] ).

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