The classic example of scientific computing is that of weather prediction. Atmospheric scientists and meteorologists use weather models (mathematical equations) that are run on fairly powerful computers. The results are generally a large amount of data (temperature, pressure, humidity, etc.) that often is plotted for various regions of the U.S. or other countries. The resulting data gives the standard forecasts that many of us look at on smart phone apps or on TV or other places.
Data analysis is also ubiquitous and one can think about this falling in the realm of scientific computing. Although this varies, an autonomous vehicle can generate 25 Gb of data per day.. Although there is plenty of scientific computing without the data generation in self-driving cars, this is an example of needing to handle and analyze large amount of data quite quickly.
This year, disease modeling came into focus with the COVID-19 pandemic. Plenty of research posted updates on predicted cases and deaths in the U.S. and throughout the world. Most of these model involve using existing data to fit mathematical functions and then use the resulting functions to predict the future. In addition, due to the diversity of conditions in different regions of the country and world, many localized models are developed at once with some predicted interaction as people travel from region to region.