Downscaling techniques are methods used to refine large-scale data into finer resolutions, making it more applicable for local analysis. These techniques are particularly important in fields like climate science, where global models may not capture local variations effectively. By using statistical or dynamical approaches, researchers can generate more precise predictions for specific regions. Common downscaling methods include statistical downscaling, which uses historical data to relate large-scale patterns to local conditions, and dynamical downscaling, which involves running high-resolution models based on global climate data. These techniques help improve the accuracy of forecasts and inform decision-making in areas such as urban planning and agriculture.