A **downscaling model** is used to change the resolution of [[Spatial Index|spatially-indexed]] data from larger-sized cells to smaller ones. While naive methods like [[Areal Interpolation|areal interpolation]] and [[Weighted Average|weighted averaging]] can lay good results in the simplest use cases, downscaling uses a [[Regression|regression model]] that uses [[Proxy Variable|proxy variables]] (that are known in the smallest desired scale) as [[Independent Variable|independent variables]] and the large-scale data as the [[Dependent Variable|target variable]]. To train such model, the large-scale is used to find a relation between the [[Proxy Variable|proxy variables]] and the data at hand. Once trained, it is applied to the small scale cells, laying an initial estimation of the small scale data. After that, a reconciliation method is used to make sure that these small-scale estimations add up to the known, large-scale data. It is important to find an adequate model for this task: decision trees are a bad choice since the cuts will be fit to the large scale samples and will have issues generalising to the small scale. Instead, [[Linear Regression|linear regressions]] or other linear methods are a better approach.