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There is much discussion in the scientific literature and concern in the wider community about climate change. Recent climate analyses indicate that the magnitude of 21st Century warming is likely to have been the largest of any century for the last 1000 years over the northern hemisphere. All the IPCC's four reports between 1990 and 2007 concluded that we cannot expect stable climate in the future and we should prepare scenarios and strategies for the survival of humankind under the conditions of forthcoming global change. In this study, the applicability of the statistical downscaling model (SDSM) in downscaling temperature in Misurata area – Libya, was investigated. The investigation includes the calibration of the SDSM model by using largescale atmospheric variables encompassing NCEP reanalysis data, the validation of the model were measured daily temperature data (1961–1990) using independent period of the NCEP reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model. The model is calibrated and applied at a daily time series, even though the output is monthly, and the prediction of the future regional maximum and minimum temperature scenarios for three time windows: 2011-2040, 2041-2070 and 2071-2099. The results showed that: The statistical downscaling model (SDSM) was able to describe the basic statistical properties of daily minimum and maximum temperature in the period of record, suggesting that it could be used to predict future trends. Trend analysis in the study area showed an increase in average annual and monthly temperature, compared to the baseline period for both HadCM3A2a and HadCM3B2a scenarios in both the dry and wet seasons. However, this increase is higher in dry months than wet months for all future time horizons and for both HadCM3A2a and HadCM3B2a scenarios. Thus, there is likely to be a significant warming in local surface temperature, which is enough for a significant change on the energy balance and is likely to affect water availability.
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