Does finer input data lead to a better model accuracy? Climate and chemical transport models all need input files including meteorology data, emission inventory, surface data (e.g. land use type) to initialize the simulation field. 

Take the case of WRF for regional meteorology data. I always use the 1 degree x 1 degree NCEP FNL analysis data in grib2 format as the model-driven data supporting the boundary and initial information every 6 hours.   

Now, I find that the ECMWF interim reanalysis data with spatial 0.25 degree x 0.25 degree can also be used as the WRF input. 

So, I'm wondering does finer input data induce better model performance? How much can be improved if the answer is true?    

I have thought out some pros of finer input file (in both spatial and temporal scale):

pros: (1) finer input field means the physical process of smaller scale(e.g turbulent transport of water) can be depicted better in some extent; (2) finer temporal variation of chemical species can emulate the real emission process better(e.g, emission related to traffic in a day). 

Are there any cons that come with the performance improvement except the computational cost?
My experience with the WRF is in tropical cyclone simulation, but here are some things to consider:


Smaller scale information might already be resolved in your higher resolution initial conditions, improving the spin-up time of effects you are trying to model
I believe the ECMWF resolution has been statistically downscaled from a 0.75 degree dataset, I might be wrong on that one though. That would mean that your 0.25 degree is just some interpolated data 
Some models perform better than others in specific regions
Higher resolution is not better if your parameterization is wrong
You can find caveats of specific datasets here: PSD Gridded Climate Datasets.