Mixed frequency regression analysis

I'm creating a multi-factor linear regression model for daily soybean futures prices (dependent variable). Most of the independent variables are daily (i.e., corn futures prices, crude oil prices, etc.). However, I would like to include some variables that are of either weekly or monthly frequency capturing data from publication of reports (exports of soybeans, production of soybeans in Brazil, etc.).

Would it be feasible to incorporate these weekly/monthly variables into the model? Given their lower frequency their values would have to repeat, given that the dependent variable is captured on a daily frequency. How should I interpret the coefficients of these lower frequency variables then? My reasoning is that I don't want to leave these variables out of the model because they do act as a shock to soybean prices when the reports are published (depending on whether the data surprised), and my sense is that there's probably "long-lasting" effect on prices until the next report is released.

I've read that MIDAS models could be a good approach here but just wondering how do those models differ from a linear regression model where the lower frequency independent variables are simply repeated along the factor columns?
Much appreciated!
I could, but obviously the downside would be that the number of observations would decrease (assuming I don’t increase the time period), and therefore lose some information value. I would like to keep it to daily data but to treat the monthly data releases (we can think of % surprise) as shocks to the dependent variable with some decaying function where, for example, a significant part of the shock occurs the day of the data release with smaller effects occurring the days following the release, until the next monthly release. So the shock is somewhat prorated throughout the days).
I hear you. Then MIDAS seems to me the only feasible option. Admittedly, however, I have very limited knowledge about it and there is only one paper on this in my library.