Abstract Submission: Climate change is studied with General Circulation Models (GCM) that have coarse grids, and require increasing their spatial resolution, process called downscaling. Civil engineering is increasingly using GCM climatic projections in their studies to comprehend future climate and its impacts. Although several downscaling methods exist, there are few comparisons among them, and to the best of our knowledge using a framework to analyze the performance of the downscaling method is a required step, which is missing in most hydrological simulation. This study analyzes the performance of eight downscaling methods, using temperature and precipitation data at different spatial scales. The analysis is performed in Chile using CR2MET V2.5 dataset, and also South America with ERA5-LAND dataset. The methods evaluated are Bilinear (BIL), Bicubic (BIC), Inverse Distance Squared (DIS), Largest Area Fraction (LAF), Nearest Neighbor (NN), First Order Conservative (CON), and Second Order Conservative (CON2). The overall framework used to test the performance of each downscaling consists in aggregating the data to different resolutions and then disaggregating them back to their original grids. Finally, performance is measured with the indices KGE, NSE, MAE, and MSE to determine which method most accurately recovers the original information. The framework to test the performance of the downscaling methods, could be used in the future to test their performance before using local climate projections for hydrological modeling.
