Earth's climate has varied due to natural causes such as the change in solar insolation, orbital motion, orogeny, and ocean circulation pattern in the past (Bytnerowicz et al., 2007; Nakicenovic et al., 2000). However, since the industrial revolution began during the 18th -century, a sharp increase in greenhouse gas emissions (i.e., carbon dioxide, methane, nitrous oxide, and ozone) have resulted in global warming and a drastic change in existing climatic conditions (Le Treut et al., 2010; IPCC 2019;). In particular, human activities associated with the burning of fossil fuels have increased carbon dioxide levels in the atmosphere from 280 ppm in 1750 to more than 400 ppm in recent years (IPCC 2019, 2021). Since the 1970s, globally averaged surface temperature data have shown a linear warming trend of ca. 0.9°C (Millar et al., 2017). Unlike temperature, which has increased globally, precipitation records indicate a variable response both in frequency and intensity (Archer & Rahmstorf, 2011; Konapala et al., 2020). Hence, there is an ongoing debate about how global warming will affect future precipitation because it has important implications for agriculture practices and food supply (Donat et al., 2016).
The lives of more than two billion people in South and West Asia depend on the summer monsoon precipitation on both short and long timescales (Clift & Plumb, 2008; Cullen et al., 2000). Therefore, understanding how the monsoons will change in the face of the anticipated increase in greenhouse gas emissions and rising global warming is a fundamental challenge. Also, the General Circulation Models (GCMs) face difficulty in simulating the regional distribution of monsoon (Turner & Annamalai, 2012) due to a multitude of physical processes and interactions that influence precipitation (Sperber et al., 2013). To reduce the uncertainty in climate models associated with monsoons and their intensity, we must understand the processes driving monsoons, seasonality, and fluctuations (Turner & Annamalai, 2012; Wang et al., 2017; Zhisheng et al., 2015).
It is suggested that the increase in greenhouse gas concentrations will intensify monsoons mainly due to an increased land-sea difference in temperature and a northward shift of the Inter-Tropical Convergence Zone (ITCZ) (Cao & Zhao, 2020; Li & Ting, 2017; Sachs et al., 2009). One such region of the world is southeastern Iran, which lies on the extreme northern border of the monsoonal domain that may be significantly affected by changes in the monsoon pattern and intensity. Southeastern Iran, straddled between the Indian Ocean Summer Monsoon (IOSM) precipitation zone and the Mid-Latitude Westerlies (MLW) precipitation zone, makes it highly sensitive to changes in climatic conditions (Hamzeh et al., 2016; Rashki et al., 2021; Vaezi et al., 2019). Furthermore, paleoclimate records indicate that intensity and variation of IOSM and MLW have changed significantly since the late Pleistocene affecting the regional hydrological conditions (Vaezi et al., 2019; Clift & Plumb, 2008; Stevens et al., 2001). Therefore, establishing a better understanding of atmospheric circulation patterns and precipitation in the distant past could help in improving our assessment of future climate change scenarios and variations in regional precipitation patterns (Mehterian et al., 2017).
GCMs have been widely used to study atmospheric patterns and eventual effects on the global and regional scales. However, output data from GCMs are typically coarse to estimate the hydrological response to climate change on a regional scale. Thus, there is a need to downscale the data from a coarse resolution in GCMs to a ‘local’ sub-grid-scale, weather station scale (Busuioc, 2008; Wilby et al., 2002), which can be achieved either by statistical or dynamical methods. Amongst the statistical downscaling methods, the Long Ashton Research Station Weather Generator (LARS-WG) has been extensively applied and tested in different climatic regions (Luo & Yu, 2012; Qian et al., 2004; Semenov et al., 2002, 2013; Semenov & Barrow, 1997; Street et al., 2009). The simulations in these studies highlight the capability and accuracy of the model in simulating climate change and projections for the future.
In the present study, as a comprehensive climate-driven investigation of the arid Iranian plateau, we developed a general understanding of the qualitative and quantitative impact of changes in precipitation and temperature pattern and their impacts. In this context, daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in the Jazmurian playa in southeastern Iran were evaluated for the distant future scenario extending from 2061-2080 using statistically downscaled outputs from the 5 GCMs with the LARS-WG model under RCPs 4.5 and 8.5. The predicted results of future hydrological changes based on different global warming scenarios are used to evaluate the performance of the models. To verify if these changes may have also occurred in the past when no direct measurements of precipitation or temperature are available, we refer to the paleoclimate study by Vaezi et al. (2019) on a sediment core from the Jazmurian playa. This study reconstructs the linkages between paleoenvironmental conditions and variability in IOSM and MLW outputs that contributed to various environmental changes in the interiors of West Asia since the late Pleistocene. We compared the variations in future atmospheric circulation and related changes in precipitation to the past cold and warm periods. The predicted simulations and paleoclimate events superimpose a complex mosaic, which can gauge our response and adaptation to climate change scenarios.