## 1 Introduction  
Fine-scale aerosol data provide essential support for air quality studies [1] and downstream health-related applications. Over the past several years, satellite-based aerosol optical depth (AOD) has been used for this purpose, primarily to estimate PM\({}_{2.5}\) surfaces at fine spatial scales [2; 3; 4]. Satellite AOD-derived PM\({}_{2.5}\) estimates have been used to examine health outcomes including respiratory [5; 6; 7] and cardiovascular [8] diseases. Generating fine-scale PM\({}_{2.5}\) from satellite AOD has several limitations including missing data due to cloud cover and bright surfaces [9], and it requires complex statistical or machine learning techniques that incorporate multiple external data sources [10]. Our study region encompasses several countries across Southwest Asia (Afghanistan, Iraq, Kuwait, Saudi Arabia, United Arab Emirates, and Qatar (Figure 1)), which is known for its extreme dry and hot hyper-arid climate. This unique environment, in addition to increased economic development and urbanization, makes both naturally and anthropogenically occurring air pollution a concern [11]. This region is also the basis of a larger research initiative assessing the impact of air quality on the health of military personnel that were deployed in the region during post 9/11 wars [12; 13]. As there is very little ground-level air quality monitoring in the region, having fine-scale aerosol data is an asset to support air pollution related research. Recent advances in data assimilation products provide a source of AOD data, with the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) drawing intensive research interest since it provides complete surfaces of AOD and related aerosol products globally from 1980 onward. Given its long time range of available data, [PERSON] et al. (2019) [14] analyzed the spatial distribution and temporal variation of MERRA-2 AOD over China from 1980 to 2017. [PERSON] et al. (2020) [15] used MERRA-2 AOD to assess natural and anthropogenic air pollution over the Middle East. However, the spatial resolution of MERRA-2 data is quite coarse (\(\sim\)50 km) which limits its application for local-scale research. In contrast, the Goddard Earth Observing System Model, Version 5 (GEOS-5) Nature Run (G5 NR) [16] can provide AOD data in finer resolution (\(\sim\)7 km). G5 NR is a global non-hydrostatic mesoscale simulation performed by the GEOS-5 Atmospheric General Circulation Model (GEOS-5 AGCM) and driven by prescribed sea-surface temperature, sea ice, surface emissions and uptake of aerosols and trace gases [16]. However, G5 NR is only available for two-years (2005-2007) due to its high computational cost which restricted its research potential. Statistical downscaling from coarse- to fine-scale is a computationally efficient solution to generate fine-scale aerosol data, which can take advantage of both the long temporal range of MERRA-2 AOD and the fine spatial resolution of G5 NR AOD. Statistical downscaling was developed primarily to generate finer spatial scale climate information from General Circulation Models (GCMs) [17], and these techniques have also been applied to remote sensing data [3; 18; 19; 20; 21]. The basic approach to statistical downscaling is that the fine (smaller) scale variable is conditioned by a coarse (larger) scale variable and local features, like topography and land-sea distribution [22]. In this perspective, the fine-scale variable can be predicted with an empirical association that relates the coarse-scale variables (predictors) and fine-scale variables (predictands). For instance, dissever is a general framework for downscaling earth resource information [18]. It uses an iterative algorithm to fit regression models between coarse- and fine-scale variables in order to optimize downscaling by ensuring the value of each coarse grid is equal to the mean of fine-scale values that are spatially covered by the corresponding coarse grid.