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resolution) Earth system models (ESMs) to make projections of possible future wind resources. However, most archives of ESM output include only daily or monthly mean wind speeds at 10 m above ground level, far below typical wind turbine hub heights ( Pryor et al. 2020a ). Further, the resolution of such models prohibits representation of the scales of motion critical to dictating wind resources. To overcome these shortcomings, either statistical or dynamical downscaling is used to obtain more accurate
resolution) Earth system models (ESMs) to make projections of possible future wind resources. However, most archives of ESM output include only daily or monthly mean wind speeds at 10 m above ground level, far below typical wind turbine hub heights ( Pryor et al. 2020a ). Further, the resolution of such models prohibits representation of the scales of motion critical to dictating wind resources. To overcome these shortcomings, either statistical or dynamical downscaling is used to obtain more accurate
surface-field predictions by accounting for the misrepresentations in physical relationships between large-scale and surface fields using historical data. In other words, the information contained in the prediction of large-scale fields is more reliable than that in surface fields, and statistical downscaling techniques can be implemented to correctly transfer this information from large-scale fields to surface fields (e.g., Scaife et al. 2014 ; Manzanas et al. 2018 ; Goutham et al. 2021 ). The
surface-field predictions by accounting for the misrepresentations in physical relationships between large-scale and surface fields using historical data. In other words, the information contained in the prediction of large-scale fields is more reliable than that in surface fields, and statistical downscaling techniques can be implemented to correctly transfer this information from large-scale fields to surface fields (e.g., Scaife et al. 2014 ; Manzanas et al. 2018 ; Goutham et al. 2021 ). The
projections are required, based on either statistical or dynamical downscaling techniques. An overview of statistical and dynamical GCM downscaling techniques for hydrologic modeling is presented in Fowler et al. ( Fowler et al. 2007 ). Statistical downscaling uses empirical relations between features reliably simulated by a GCM at gridbox scales and surface predictands at subgrid scales. Dynamical downscaling uses simulations from regional climate models with initial and lateral boundary conditions from
projections are required, based on either statistical or dynamical downscaling techniques. An overview of statistical and dynamical GCM downscaling techniques for hydrologic modeling is presented in Fowler et al. ( Fowler et al. 2007 ). Statistical downscaling uses empirical relations between features reliably simulated by a GCM at gridbox scales and surface predictands at subgrid scales. Dynamical downscaling uses simulations from regional climate models with initial and lateral boundary conditions from
-spatial-scale structure using the original coarse-resolution fields along with finer-scale observations, topography, and dynamics. There are two main types of downscaling: dynamical, which uses regional climate models driven at the domain boundaries by global climate model output, and statistical, which uses the historic relationships between large- and small-scale conditions. Dynamical methods can capture nonstationary relationships between the large- and finescale that may develop in the future and can produce more
-spatial-scale structure using the original coarse-resolution fields along with finer-scale observations, topography, and dynamics. There are two main types of downscaling: dynamical, which uses regional climate models driven at the domain boundaries by global climate model output, and statistical, which uses the historic relationships between large- and small-scale conditions. Dynamical methods can capture nonstationary relationships between the large- and finescale that may develop in the future and can produce more
1. Introduction Downscaling (or right scaling) of climate information is conducted primarily to advance understanding of processes affecting climate that are not resolved well by global models and/or provide decision-makers responsible for adaptation actions with salient and credible information about possible climate change within their specific region/locality of interest ( Giorgi 2019 ; Gutowski et al. 2020 ; Maraun and Widmann 2018 ). Empirical–statistical downscaling (ESD) methods are
1. Introduction Downscaling (or right scaling) of climate information is conducted primarily to advance understanding of processes affecting climate that are not resolved well by global models and/or provide decision-makers responsible for adaptation actions with salient and credible information about possible climate change within their specific region/locality of interest ( Giorgi 2019 ; Gutowski et al. 2020 ; Maraun and Widmann 2018 ). Empirical–statistical downscaling (ESD) methods are
1. Introduction Downscaling allows high-resolution information to be extracted from large-scale models, particularly under future scenarios for which no observational data are available. There are two main classes of downscaling: statistical and dynamical. Dynamical downscaling typically employs the use of a regional climate model (RCM) embedded within, or driven by output from, a larger-scale global model. Giorgi (2006) presents a more complete description of RCMs and dynamical downscaling
1. Introduction Downscaling allows high-resolution information to be extracted from large-scale models, particularly under future scenarios for which no observational data are available. There are two main classes of downscaling: statistical and dynamical. Dynamical downscaling typically employs the use of a regional climate model (RCM) embedded within, or driven by output from, a larger-scale global model. Giorgi (2006) presents a more complete description of RCMs and dynamical downscaling
Statistical Downscaling of Wintertime Temperatures over South Koreafrom the difficulty of prescribing open boundary conditions ( Giorgi 1990 ; Jones et al. 1995 ; Christensen et al. 1997 ; Marchesiello et al. 2001 ). A regional climate model (RCM) simulation is often inadvertently affected in a significant manner by the natural variability in a GCM output introduced through open boundary conditions. Statistical downscaling is also common and is a simple alternative to dynamical downscaling ( Hewitson and Crane 1996 ; Wilby and Wigley 1997 ; Wilby et al. 1998
from the difficulty of prescribing open boundary conditions ( Giorgi 1990 ; Jones et al. 1995 ; Christensen et al. 1997 ; Marchesiello et al. 2001 ). A regional climate model (RCM) simulation is often inadvertently affected in a significant manner by the natural variability in a GCM output introduced through open boundary conditions. Statistical downscaling is also common and is a simple alternative to dynamical downscaling ( Hewitson and Crane 1996 ; Wilby and Wigley 1997 ; Wilby et al. 1998
greenhouse gases on the global climate system ( Alley et al. 2007 ). GCM simulations of future climate through 2099 project a wide range of possible scenarios ( Alley et al. 2007 ) but often overlook numerous climatological details necessary for hydrologic modeling at the basin scale because of their coarse resolution ( Wigley et al. 1990 ; Carter et al. 1994 ; Xu 1999 ). Statistical or dynamical methods can be used to downscale information from coarse-resolution GCMs to the basin scale for hydrologic
greenhouse gases on the global climate system ( Alley et al. 2007 ). GCM simulations of future climate through 2099 project a wide range of possible scenarios ( Alley et al. 2007 ) but often overlook numerous climatological details necessary for hydrologic modeling at the basin scale because of their coarse resolution ( Wigley et al. 1990 ; Carter et al. 1994 ; Xu 1999 ). Statistical or dynamical methods can be used to downscale information from coarse-resolution GCMs to the basin scale for hydrologic
techniques. Many downscaling models have been developed in the past few decades, which all have strengths and weaknesses. Several research papers offer discussions on the various methodologies, the most notable among them being the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Solomon et al. 2007 ; Wigley 2004) . Statistical downscaling, which is the main emphasis in this paper, derives a statistical or empirical relationship between the large-scale climate features
techniques. Many downscaling models have been developed in the past few decades, which all have strengths and weaknesses. Several research papers offer discussions on the various methodologies, the most notable among them being the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report ( Solomon et al. 2007 ; Wigley 2004) . Statistical downscaling, which is the main emphasis in this paper, derives a statistical or empirical relationship between the large-scale climate features
resolution ( Zhang et al. 2011 ). Increasing daily climate simulation spatial resolution to serve this purpose can be accomplished via postprocessing methods incorporating weather station observations such as interpolation (e.g., kriging, bilinear), constructed statistical models (e.g., Daymet; Thornton et al. 1997 ), or multisite weather generators ( Wilks 2009 ). Alternatively, regional climate models (RCMs) offer the ability to dynamically downscale meteorological variables at varying resolutions
resolution ( Zhang et al. 2011 ). Increasing daily climate simulation spatial resolution to serve this purpose can be accomplished via postprocessing methods incorporating weather station observations such as interpolation (e.g., kriging, bilinear), constructed statistical models (e.g., Daymet; Thornton et al. 1997 ), or multisite weather generators ( Wilks 2009 ). Alternatively, regional climate models (RCMs) offer the ability to dynamically downscale meteorological variables at varying resolutions
