ABSTRACT. The annual rainfall in the southwestern region of Saudi Arabia was analyzed. The study area was divided into regions and subregions depending on the altitude above mean sea level and mean annual rainfall. The classification was supported by comprehensive statistical analysis. The results showed that spring is the main season for rainfall, followed by summer. The gamma distribution was found to provide the best fit, followed by the normal distribution. The analysis of interannual and relative interannual variability showed that regions I and V (adjacent to and furthest away from the coast, respectively, in the southern part of the study area) can be classified as arid, while regions 11, 111 and IV (between and to the north of regions 1 and V) are classified as semi-arid.

 The Eastern Mediterranean and the Middle East region is warming almost two times faster than the global average and more rapidly than other inhabited parts of the world. Climate projections indicate a future warming that is particularly strong in the summer, associated with unprecedented heat extremes. Total precipitation will likely decrease in many regions, particularly in the eastern Mediterranean. Virtually all socio-economic sectors will be critically affected by the projected changes.

This paper investigates the effect of large-scale forcings on the variability of the mean air temperature of Saudi Arabia, using the observed data and the National Centers for Environmental Prediction (NCEP) reanalysis gridded datasets for the period 1978-2010. The analysis shows that the seasonal mean temperature variability is high in the northern and central regions compared to the southern. Moreover, the temperature variability is highest (~30%) in the winter season and lowest (~2 %) in the summer. Following global warming indications, the interannual variability of the mean air temperature of Saudi Arabia indicates a warming phase that started in the late 1990s.The analysis reveals that the strong variability in temperature over Saudi Arabia is closely associated with the North Atlantic Oscillation (NAO) index for all seasons except the autumn; however, the relationship is most prominent during the winter season. The study indicates that the winter season temperature is also influenced by the Arctic Oscillation (AO) index, whereas the spring season temperature is influenced by the El Niño Southern Oscillation (ENSO). This research concludes that the negative phases of ENSO, AO and NAO all play a major role in the temperature increase over most parts of Saudi Arabia.

At global scale, lakes are warming faster than both the atmosphere and the oceans (O'Reilly et al., 2015). Given that, all lakes are land-locked, these changes could be intensified by the projected changing climate under the future influence of anthropogenic stressors (Jane et al., 2021;Modabberi et al., 2020;Woolway et al., 2021). This could have deep implications for the lake ecological services (Kraemer et al., 2021;Pilla et al., 2020) and threaten both the quantity and quality of the most important sources of freshwater for humankind (Kraemer et al., 2015;Noori et al., 2018). Despite the global warming impact on the lakes, the rate of increase in the lake surface-water temperature (LST) is not consistent with that in the lake deep-water temperature (LDT). O'Reilly et al. ( 2015) calculated a significant global mean warming rate of 0.34°C per decade in LST, while no such significant warming was found in the LDT at global scale (Kraemer et al., 2015;Pilla et al., 2020). These results suggest a progressive divergence between the LST and LDT at global scale that intensifies both strength and duration of lake thermal stratification (Oleksy & Richardson, 2021).Although there is consensus on the increase in LSTs globally, the changing rates are not consistent even in a local geographical region. Studies show both cooling and warming trends in the LST in high-latitude lakes (e.g., in Alaska and Northern Europe), high altitude lakes (e.g., in Tibetan Plateau), and temperate lakes (e.g., in Central Europe; Kraemer et al., 2015;O'Reilly et al., 2015; Wan et al., 2018). Regarding the LDT, relatively few longterm databases on freshwater lakes are available compared to the LST. They mainly cover three or four decades. On the other hand, the changes reported in relatively LST-rich databases cannot simply reveal the more complex nature of LDT influenced by multiple stressors. Pilla et al. (2020) concluded that the trends in the LDT were not explained by those observed in the LST and thermal stability. Thus, we understand less about changes in the LDT than the LST (Richardson et al., 2017).