How reanalysis data influence our research/results?
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I don't think I fully understand your question, but I'll try to answer it. Reanalysis data is the use of weather models and data assimilation to piece back the weather. It is a 4-dimensional dataset (Latitude, longitude, pressure levels, and time). You can do whatever you wish with that data (provided you follow the legal guidelines with using the ERA dataset), but know that it isn't perfect; it usually is at a coarse resolution, and doesn't always contain everything you may want. Often reanalysis data can be used as input into other meteorological models, such as the WRF model.

As far as a random forest model, it really depends on what your intentions and design are. Admittedly, I don't know much about random forest model or your experimental design. To answer the question of whether the data could be used as explanatory variables, sure if you have some data that isn't within the dataset. But be aware of the limitations. There are other highly used reanalysis datasets, such as the CFSR and MERRA-2 and a variety of other reanalysis datasets, but those have similar limitations too.