Intra-urban fine-scale data and models are needed to understand infrastructure interactions that shape equity and health related to extreme heat, cold and precipitation events. Fine-scale data are needed to address spatial equity at the scale of city blocks or block groups where income and race data are available. We conducted nested simulations with the Weather Research and Forecasting (WRF) model that cover parts of the US state of Minnesota — one of the fastest warming states in the contiguous US. The first two nests at 5km and 1km horizontal resolution cover the counties of southern Minnesota, with the outer 5km grid also covering some counties in the neighboring states Iowa and Wisconsin. Within the 1km inner grid, we created two additional nests. The third grid covers the metropolitan region of the Twin Cities Minneapolis and Saint Paul at 200m resolution. Within this grid, we created a fourth nest over a 4x4km neighborhood in downtown Minneapolis that includes the campus of the University of Minnesota at 40m resolution. All model nests have 82 vertical levels. Lateral input data were acquired from the global Coupled Forecast System (CFS) analysis at 30km horizontal resolution. The boundary conditions consist of high-resolution land use data including vegetation types, urban fraction, building heights and shapes. The input data were specifically designed by the Remote Sensing and Geospatial Analysis Laboratory at the University of Minnesota and cover the whole Twin Cities Metro Area (TCMA) at 1m horizontal resolution. This dataset was derived from a multi-temporal composite of aerial imagery from the summer of 2015 and fall of 2009-2011, and lidar data of 2011 and 2012. The vertical accuracy of the lidar data meets or exceeds 12.5cm root mean square error (RMSE). The results of our model simulations show remarkable fine-scale climate responses to changes in vegetation cover and albedo that are going to be used for various urban planning projects.