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

===== 10.3.1.3.1 Perfect prognosis =====

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Perfect-prognosis models are statistical models calibrated between observation-based large-scale predictors (e.g., from reanalysis) and observed local-scale predictands ( [[#Maraun--2018b|Maraun and Widmann, 2018b]] ). Regional climate projections are then generated by replacing the quasi-observed predictors by those from climate model (typically global model) projections. Predictor patterns that are common to observations and climate model data can be defined by common empirical orthogonal functions ( [[#Benestad--2011|Benestad, 2011]] ). The perfect prognosis approach can either be used to generate daily (or even sub-daily) time series, or local weather statistics (e.g., [[#Benestad--2018|Benestad et al., 2018]] ).

Regression-like models ( [[#Maraun--2018b|Maraun and Widmann, 2018b]] ) rely on a transfer function linking an observed local statistic (such as the temperature at a given day) to some set of large-scale predictors. Recent developments include stochastic regression models to explicitly simulate local variability ( [[#San-Martín--2017|San-Martín et al., 2017]] ; those explicitly modelling temporal dependence are assessed in [[#10.3.1.3.4|Section 10.3.1.3.4]] ). The use of machine learning techniques has been reinvigorated, including genetic programming to construct a data-driven model structure ( [[#Zerenner--2016|Zerenner et al., 2016]] ) and deep and convolutional neural networks ( [[#Reichstein--2019|Reichstein et al., 2019]] ; [[#Baño-Medina--2020|Baño-Medina et al., 2020]] ).

Analogue methods ( [[#Martin--1996|Martin et al., 1996]] ; [[#Maraun--2018b|Maraun and Widmann, 2018b]] ) compare a simulated large-scale atmospheric field with an archive of observations and select, using some distance metric, the closest observed field in the archive. The downscaled atmospheric field is then chosen as the local atmospheric field observed on the instant the analogue occurred. New analogue methods have been developed to simulate unobserved values including a rescaling of the analogue ( [[#Pierce--2014|Pierce et al., 2014]] ) or by combining analogues and regression models ( [[#Chardon--2018|Chardon et al., 2018]] ).

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