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<p>The downscaling approaches: Statistical and Dynamic, developed for regional climate predictions, have both advantages and limitations. The statistical downscaling is computationally inexpensive but suffers from the violation of the assumption of stationarity in statistical (predictor-predictand) relationship. The dynamical downscaling is assumed to take care of stationarity but suffers from the biases associated with various sources.  Here we propose a joint approach of both the methods by applying statistical methods: bias correction & statistical downscaling to <strong>Coordinated Regional Climate Downscaling Experiment (</strong>CORDEX) evaluation runs. The evaluation runs are considered as perfect simulations of CORDEX Regional Climate Models (RCMs) with the boundary conditions by ERA-Interim reanalysis data. The statistical methods are also applied to ERA-Interim reanalysis data and compared with observation data for Indian Summer Monsoon characteristics. We evaluate the ability of statistical methods under the non-stationary environment by taking the difference of years close to extreme future runs (RCP8.5) as warmer years and preindustrial runs as cooler years. We find statistical downscaling of CORDEX evaluation runs shows skill in reproducing the signal of non-stationarity. The study can be extended methods by applying statistical downscaling to CORDEX RCMs with the CMIP5 boundary conditions. </p>

<p>The world needs energy for its social and economic development. In the growing population and industrialization, there is an increasing demand for energy worldwide. The fossil fuel resources are still major resources for fulfilling this energy demand though they are responsible for the increased GHG emissions. Renewable energy is an alternative and greener approach towards meeting increasing energy demand. The wind energy is one of the most prominent resources of greener and renewable energy. The islands of Mauritius and Reunion in the southwest Indian Ocean are blessed with wind resources. The wind energy can be used to meet the demand of energy requirement of these two islands by increasing the number of wind turbines. However, energy generation with wind turbines is sensitive to the variability in the surface wind due to climate variability. The surface wind data available is sparse due to limited ground-based observation. The data quality is also affected by instrumental errors, and data is available only for past and present. Regional Climate Models (RCMs) are the main source of climate information for the present and the future. However, simulations from RCMs deal with biases from various sources and therefore need to bias-corrected. Here we use a transfer function based on the method proposed by Li et al. (2010) for the bias-correction of surface wind over Reunion and Mauritius islands. For this purpose, RegCM4.7 RCM from CORDEX AFR22 domain has been chosen for the time period of 1981-2004. The data is interpolated at 9 km resolution and bias-corrected with respect to surface wind data obtained from ERA5 land reanalysis data. The bias-corrected results are validated with the ERA5 land reanalysis data set.</p>

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