The aim of this study is to assess a different approach of SWAT model setup with enhanced Hydrologic Response Unit (HRU) definition procedure. The method uses a set of customizable MATLAB scripts (hence, SWAT-LAB) to produce HRUs from a combination of topographic, soil, landuse and administrative unit rasterized datasets. We used this approach to setup, run and calibrate a large-scale transboundary Nemunas River model, consisting of eleven sub-models. Nemunas River is the major contributory that discharges into the Curonian Lagoon, which is the largest European coastal lagoon. Belarus, Lithuania, Poland and the Russian Federation Kaliningrad Oblast share Nemunas River basin area. The basin is experiencing nutrient load from different sources in the riparian countries, nevertheless, the burden of improving the water quality of the river falls mainly on Lithuania. This article focuses on assessing the practicability of SWAT-LAB for creating a high-resolution large-scale transboundary hydrological and water quality model, where data availability is limited and fragmented. We demonstrate the model performance on a case study of one of the Nemunas River tributaries: Neris or Vilija River basin, which is situated largely in the Republic of Belarus. Model performance was evaluated graphically, using hydrographs and percent exceedance curves, and quantified using coefficient of determination (R-2) and Nash-Sutcliffe efficiency (NS). We achieved good model performance for monthly (calibration R-2 = 0.80, NS = 0.83; validation R-2 = 0.80, NS = 0.76) and daily (calibration R-2 = 0.66, NS = 0.66; validation R-2 = 0.67, NS = 0.66) flow. Satisfactory performance results were achieved in modeling monthly loads of suspended sediments (calibration R-2 = 0.4, NS = 0.44; validation R-2 = 0.58, NS = 0.34), total phosphorus (calibration R-2 = 0.71, NS = 0.61; validation R-2 = 0.53, NS = 0.56) and total nitrogen (calibration R-2 = 0.55, NS = 0.42; validation R-2 = 0.48, NS = 0.4). Furthermore, we use the model to assess possible future nutrient loads that could be transported from Belarus to Lithuania under two climate change scenarios (RCP 4.5 and 8.5) and present the results.