The analysis of statistical downscaling methods has become an active area of hydrological research in recent years because of the potential to investigate climate change impacts at the hydrological scale. In particular the applicability of downscaling methods to remote and often data sparse regions provides a significant challenge to hydrology, not least because such remote regions are often perceived to be vulnerable to the impacts of climate change. The research has considered the potential of using remote sensing, reanalysis and other rainfall and climate data products to overcome some of the issues of data scarcity and quality before evaluating the climate teleconnections within the tropical Andes of South America. The main conclusions of the research are that remote sensing products may provide a useful addition to rainfall runoff modelling studies, but are not applicable to downscaling studies because of their short duration. The TRMM 3B42 product was found to provide a better representation of river runoff than the PERSIANN product when routed through a calibrated hydrological model, suggesting that this product in particular may be useful in sparsely gauge regions. The main conclusions of the statistical downscaling were that the GlimClim downscaling model may be applied to a remote region, but that some of the model assumptions mean that it is often difficult to achieve a good model fit. Additional conclusions relate to the propogation of uncertainty through the modelling chain with respect to the simulation of the future A1B climate scenario. 10 GCMs were used to evaluate the climate uncertainty, with the envelope of simulations showing an increase for future time slices (2020’s, 2050’s and 2080’s) compared with the current 20C3M emissions scenario. However, all GCMs showed that there is a projected decrease in rainfall and runoff.