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

===== 10.3.3.7.1 Performance of perfect prognosis methods =====

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Perfect prognosis methods can perform well when the synoptic forcing (i.e., the explanatory power of large-scale predictors) is strong ( [[#Schoof--2013|Schoof, 2013]] ). Using this approach, downscaling of precipitation is particularly skilful in the presence of strong orographic forcing. The representation of daily variability and extremes requires analogue methods or stochastic regression models, although the former typically do not extrapolate to unobserved values ( [[#Gutiérrez--2019|Gutiérrez et al., 2019]] ; [[#Hertig--2019|Hertig et al., 2019]] ). Temporal precipitation variability is well-represented by analogue methods and stochastic regression, but analogue methods typically underestimate temporal dependence of temperature ( [[#Maraun--2019b|Maraun et al., 2019b]] ). Spatial dependence of both temperature and precipitation is only well-represented by analogue methods, for which analogues are defined jointly across locations, and by stochastic regression methods explicitly representing spatial dependence ( [[#Widmann--2019|Widmann et al., 2019]] ). Overall, there is ''high confidence'' that analogue methods and stochastic regression are able to represent many aspects of daily temperature and variability, but the analogue method is inherently limited in representing climate change ( [[#Gutiérrez--2013|Gutiérrez et al., 2013]] ).

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