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Knowing of precipitation values in different regions is always of main and strategic issues of human which has important role in short- term and long-term decisions. In order to determine of precipitation model and forecasting it, there are different models, but given that the precipitation data have a spatial autocorrelation, the spatial statistic is a powerful tool to recognition of spatial behaviors. In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. Results showed a powerful correlation between precipitations with geographical factors. Also results of modeling through OLS and GWR representative that forecasting of GWR is close to reality, so that in GWR, the sum of errors of residuals is less, the  is more and there aren't any spatial autocorrelation in residuals and the residuals are normal. The of OLS can only justify 75 percent of precipitation variations with spatial factors while in GWR this quantity is 82- 97 percent. Accordingly, it was found that, in east, northeast and north of province the altitudes, in east and northeast and Zagros Mountains the view shade and slope are the most important spatial factors, respectively.

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Considering the vast application of sea surface temperature in climatic and oceanic investigations, this parameter was studied in Oman Sea from 1986 to 2015. The SST was surveyed using trend analysis and Global and local Moran’s I spatial autocorrelation. In trend analysis, the Mann-Kendall test was used to determine the trend of SST changes and the Sen's Estimator method was used to examine the slope of the changes. Using these methods, it was found that during January, February and December, there was no significant ascending trend in SST values, and only parts of the Strait of Hormuz had a significance descending trend. On the other hand, there was no significant descending trend in March, and the ascending trend in the SST was seen in the southern part of the Oman Sea. Other months of the year had a significant ascending and descending trend in different parts of the Oman Sea, which October had the highest ascending trend. In the annual time scale, it was also found that the southern parts of the Oman Sea had ascending trend in the SST value and Western parts had a descending trend. The occurred changes in the high amounts (positive and negative) were corresponding to the significance ascending and descending trends. The results of Global Moran for the annual time scale indicated an ascending trend of autocorrelation values and cluster patterns of SST data over time, using the local Moran analysis, it was found that warm clusters of SST are increasing in the Oman Sea, and on the other hand, cold clusters of this parameter have been reduced over 30 years. According to the results of trend and spatial autocorrelation analysis, it has been found that SST have been increasing in different parts of the Oman Sea during 30 years, so climate change and global warming may have affected this region.