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Remotely sensed data are often used as proxies for indirect precipitationmeasures over data-scarce and complex-terrain areas such as the PeruvianAndes. Although this information might be appropriate for some researchrequirements, the extent at which local sites could be related to suchinformation is very limited because of the resolution of the availablesatellite data. Downscaling techniques are used to bridge the gap betweenwhat climate modelers (global and regional) are able to provide and whatdecision-makers require (local). Precipitation downscaling improves the poorlocal representation of satellite data and helps end-users acquire moreaccurate estimates of water availability. Thus, a multifractal downscalingtechnique  complemented by a heterogeneity filter was applied to TRMM (TropicalRainfall Measuring Mission) 3B42 gridded data (spatial resolution~ 28 km) from the Peruvian Andean high plateau or \textit{Altiplano}to generate downscaled rainfall fields that are relevant at an agriculturalscale (spatial resolution ~ 1 km)

Spatial random downscaling of rainfall signals in Andean heterogeneous terrain