## 1 Introduction
Deep learning [23] has surpassed traditional statistical approaches with considerable performance improvements, and has thus been used in a variety of remote sensing data applications [24]. The convolutional neural network (CNN) is a popular method for downscaling due to its ability to learn spatial features from large gridded data [25]. Recently, a CNN-based model called the Super Resolution Deep Residual Network (SRDRN), which utilized convolutional layers and residual networks, was developed to downscale daily precipitation and temperature [26]. Autoencoder-like models with residual connections and parameter sharing have also been used to downscale by incorporating an iterative training strategy to force spatial value consistency [27]. Networks with transfer learning have been used in a spatial context to generalize the empirical associations within one region to apply downscaling in a different region, showing notable improvement compared to classical statistical downscaling methods [26; 28]. We propose an artificial neural network (ANN) [29] sequential downscaling method (ASDM) with transfer learning enhancement (ASDMTE). ASDM/ASDMTE utilizes empirical between-scale associations, and accounts for inherent within-scale temporal associations among fine-scale data. In addition, within-scale temporal associations in the coarse-scale data being downscaled are integrated into the ASDMTE model through the use of transfer learning to enhance downscaling performance. Under the ASDM framework, the fine-scale variable can be modeled as a non-linear function of coarse-scale variable, with a sequence of temporally lagging fine-scale variables at the same location adjusting for geographic information (e.g., elevation), time (day of the year) and location (latitude, longitude). To enhance the performance of ASDM, transfer learning can be incorporated where another similar sequential ANN model is trained on the long time series of coarse-scale data to learn its inherent temporal associations; this model is then transferred into ASDM to enhance its downscaling performance. We developed ASDM/ASDMTE models to downscale AOD data obtained from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), a satellite-based reanalysis product produced by NASA's Global Modeling and Assimilation Office (GMAO). MERRA-2 data are available for a long period (1980-present) at relatively coarse scale (\(\sim\)50 km). The target for downscaling was fine-scale (\(\sim\)7 km) AOD from the Goddard Earth Observing System Model, Version 5 (GEOS-5) Nature Run (GSNR), another satellite-based product [16]. At this resolution, GSNR is an informative data source for understanding local-scale air quality and as an exposure metric for health effects studies, but it is limited in temporal range (2005-2007), which restricts its broad use for long-term studies. As the fine-scale GSNR data has limited temporal range (2 years of daily data), it was difficult to build stable empirical associations needed for traditional statistical downscaling that link large-scale variables with local-scale variables. Furthermore, little external or covariate information were available at fine scales that could help with traditional downscaling. These limitations made it impractical to establish between-scale empirical associations without other prior knowledge, particularly since the single coarse-scale variable did not have enough spatial variability to predict the fine-scale variable. Lastly, even though GSNR and MERRA-2 provide the same variables over the same region and period of time, they are independent datasets that do not match on a point-to-point basis due to algorithmic differences [16]. Specifically, the mean of the GSNR 7 km grid values is not exactly equal to its coincident MERRA-2 50 km coarse grid value. Figure 1: Map of the study domain. We applied our ASDM and ASDMTE downscaling approaches to G5 NR and MERRA-2 data for several countries in Southwest Asia (Figure 1).