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Mean annual precipitation (mm) for Northern Ireland (NIR), 1961–1990 (after Betts 2002b). Also shown are locations of places mentioned in text  

Mean annual precipitation (mm) for Northern Ireland (NIR), 1961–1990 (after Betts 2002b). Also shown are locations of places mentioned in text  

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Statistical downscaling bridges the gap between General Circulation Model (GCM) output and climate-impacts modellers' requirements. Recent development of user-friendly software has facilitated use of statistical downscaling methods by the wider climate-impacts community. Simplicity of use, however, does not imply underlying conceptual simplicity. O...

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... In addition to model structure, downscaled series are also sensitive to the choice of predictor variables (Crawford et al. 2007;Borges et al. 2017). Other work warns about equifinality and transferability issues-whereby alternative predictor sets yield equivalent skill during model calibration and validation but different projections when applied under changed climate conditions (Fu et al. 2018). ...
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... material for reference. Although many statistical downscaling comparisons have focused on the methodology applied to a single predictor set(Gutmann et al. 2014;Dixon et al. 2016;Lanzante et al. 2018), other studies have highlighted the importance of predictor set Accepted for publication in Journal of Hydrometeorology. DOI 10.1175/JHM-D-21-0142.1.Crawford et al. 2007;González-Rojí et al. 2019), thus a comparison evaluating both the predictor selection and the estimation algorithm is a valuable next step.e. MetricsIn evaluating climate downscaling methods, there are several key metrics to assess. First and foremost, climate downscaling methods must predict the expected mean and variability observed in ...
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... GCMs predictors and local predictant are considered as inputs and output components of transfer functions, respectively. In these downscaling methods, the most crucial step is to discover the most reliable predictors to establish a statistical relationship (Crawford et al. 2007). Many researchers consider a physical relation as a proper criterion while some of them express that behavior of rainfall is a consequence of one specific variable (Goyal and Ojha 2012), so they selected single ones or considered a set of predictors. ...
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... For a more detailed analysis of the predictors and in order to identify potential better correlations with the historical records, the lagging of daily predictor variables could be applied as well as suggested for some authors (e.g., Harpham and Wilby 2005;Crawford et al. 2007) with the purpose of revealing hidden direct relationships between predictand and predictors; this is because predictors from distant grid-boxes may also influence the local climate in distinct time. ...
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... The choice of predictors and the selection of grid boxes in the LSV domain have an important impact on the performance of the downscaling (Crawford et al., 2007;Liu et al., 2013;Rashid et al., 2016). Many predictor selection methods were proposed by previous literatures, including circulation indices (Fan et al., 2015), wavelet techniques Rashid, 2016), step-wise screening (Wilby et al., 2002) and principal component analysis (PCA) (Huth, 1999). ...
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Statistical downscaling (SD) of daily precipitation is a challenging task, and the identification of predictors is crucial for constructing SD models. This study focuses on identifying SD predictors for summer (June–September) daily precipitation in China. Six large-scale variables (LSVs) in ERA-Interim reanalysis were used to select predictors for 177 sites. For each site, the predictor identification was conducted by searching the grid box having the best correlation to precipitation in a three-dimensional way: across different grid boxes and multiple pressure levels. The result indicates that correlations are often sensitive to the pressure levels. Adjacent sites share similar spatial patterns of correlations, indicating regionally different physical relations between LSVs and precipitation. The predictor selection reasonably reflects the regional circulations related to precipitation. Twelve candidate predictors were used to train generalized linear models by least absolute shrinkage and selection operator (LASSO) algorithm. The validation indicates the models have generally high performance, and also shows relatively poor performance for the sites in North China, Northwest China, and Yunnan when compared to that in the east of China. The downscaled outputs can roughly reflect the annual variations of summer total precipitation and rainy days. Two experiments on the stationarity assumption of the models under different climate conditions were conducted, indicating that no areas/sites were found significantly violated the stationarity assumption. This study presents guidance on how to select suitable predictors for downscaling daily precipitation in different areas of China.
... Downscaling results may be sensitive to the predictor domain under consideration (Benestad, 2002;Mtongori et al., 2016). Many researchers have experimented with the effect of the domain size in their downscaling studies (Timbal and McAvaney, 2001;Wetterhall et al., 2007;Crawford et al., 2007;Yang et al., 2013). Benestad (2002) reported that the choice of predictor domain has a significant role in the downscaling process where a smaller area of the predictor domain may provide useful downscaling results over a target region. ...
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Selection of suitable predictors for downscaling local-scale precipitation from the wide range of large-scale predictors available in National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) reanalysis is a challenging task because of the existence of the complex interactions between local-scale predictands and large-scale predictor fields. An attempt was made to assess how well different large-scale predictors were able to reproduce local-scale monsoon precipitation over seven homogeneous zones of India through statistical downscaling. For calibration of downscaling (DS) models, the principal component (PC)-based multiple linear regression approach was adopted where each raw grid-point predictor field transformed into PCs using empirical orthogonal function (EOF) analysis. The predictors consistently producing better downscaled results across four nonoverlapping calibration and validation periods were identified as “superior predictor” (SP). It was found that some common predictors like precipitable water; specific and relative humidity at different levels have emerged as SP predictors over several zones. In general, SP predictors have not been much sensitive with small changes in the domain size. However, a decline in performances of DS models was noticed for the majority of SP predictors for a large increase in the size of domains. Especially, the largest South Asia domain has been the most inappropriate domain as very few predictors found to be suitable for downscaling. In general, about 40% out of 36 numbers of combined predictors were identified as potential SP predictors over the majority of the zones. Several numbers of combined SP predictors have also produced slightly superior skills compared to single SP predictors. In many cases, predictors showing poor performance as single predictors have produced improved performances when combined with other predictors.
... Comparison studies demonstrated that SDSM satisfactorily simulates near-surface air temperature, the annual precipitation cycle and seasonal and annual precipitation totals (Wilby and Dawson, 2013), also in regions with high seasonal climate variability such as DF. However, the simplicity of the statistical downscaling can lead to false perception of the underlying concept and, therefore, to unreliable representations of future climate (Crawford et al., 2007;Hewitson et al., 2014). This study addresses several aspects for a credible application of a statistical downscaling model (i.e. ...
... The information of neighboring grid-boxes is not independent and spatial offsets between predictands and predictors may exist. Predictands are also related to predictors from neighboring grid-boxes rather than only from the overlying grid-box (Wilby and Wigley, 2000;Brinkmann, 2002;Crawford et al., 2007). There are some techniques in reducing the dimensionality of the predictor field such as canonical correlation analysis or principal component analysis (Maraun et al., 2010). ...
... Distant grid-boxes may influence the local climate in distinct time. The main idea of backward and forward lag is to substitute observation in space for observation in time (Harpham and Wilby, 2005;Crawford et al., 2007). This concept reflects atmospheric processes in DF. ...
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As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson's correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.
... Some of them have been widely used in statistical downscaling context with good results. For instance, surface variables such as the temperature at 2 m (T2), the sea level pressure (SLP) or atmospheric variable as the geopotential height, the zonal and meridional wind components and relative humidity at 850 hPa (Z850, U850, V850, R850) can be found in studies like Cavazos et al. (2005) or Crawford et al. (2007). The dew point at 2 m (D2) was also added. ...
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Given the coarse spatial resolution of General Circulation Models, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. This need for high-resolution data leads to apply projection techniques called downscaling. Downscaling can be performed according to two approaches: dynamical and statistical models. The latter approach is constituted by various statistical families conceptually different. If several studies have made some intercomparisons of existing downscaling models, none of them included all those families and approaches in a manner that all the models are equally considered. To this end, the present study conducts an intercomparison exercise under the EURO- and MED-CORDEX initiative hindcast framework. Six Statistical Downscaling Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The downscaled simulations are driven by the ERAinterim reanalyses over the 1989-2008 period over a common area at 0.44° of resolution. The 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. For each aspect, one or several indicators are computed to discriminate the models. The results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based SDMs, while spatial and temporal variability are better modelled by RCMs and resampling-based SDM. These general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. The indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. Nevertheless, building on previous intercomparison exercises, this study provides a consistent intercomparison framework, including both SDMs and RCMs, which is designed to be flexible, i.e., other models and indicators can easily be added. More generally, this framework provides a tool to select the downscaling model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models.