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Two downscaling models in the generation of climate change scenarios in the Mexicali valley in Mexico were evaluated. The evaluated techniques were the stochastic weather generator (LARSWG) and the dynamical-statistical model (SDSM). Each technique was evaluated for its ability to reproduce some statistical characteristics of the observed climate during the period 1961-1990. Climate change scenarios of temperature and precipitation for the period 2020 to 2050 were considered. LARS WG and SDSM are simple techniques that showed different abilities in simulating statistical moments of the observed climate, but both were more efficient for simulating temperature than precipitation. For one of evaluated scenarios differences of 0.1°C and 0.2°C for maximum and minimum temperature and 40.4% to 76.7% for precipitation.

Evaluación de dos Modelos de Reducción de Escala en la Generación de Escenarios de Cambio Climático en el Valle de Mexicali en México