The most important factors in sales are typically promotions, price changes and markdowns, followed by seasonality (intra-yearly and intra-weekly) or lifecycles, depending on what you sell. (Grocery has yearly seasonality, fashion and consumer electronics have lifecycle effects.)
I would thus recommend that you account for these effects first. For instance, you could use Fourier terms to account for seasonality, with day-of-week dummies to account for the fact that retail sales are typically higher on Saturday than on other days of the week. Include prices, price changes and promotions as regressors. You may want to model the residuals using an ARIMA model or similar. Finding a good model for these main drivers can certainly be a whole project all by itself.
Then, and only then, you are ready to investigate how much explanatory power weather has on top of all these effects, since your managers presumably know about promotions and seasonality and should not be surprised by them. So you could take the residuals from the above model and regress those on your weather information. (You could also run one big model that includes all covariates simultaneously, and then test the nested models.)
I'd recommend that you include your weather information either as-is, or spline-transformed. Splitting, e.g., temperature information into "hot" vs. "not hot" models the effect that sales change abruptly above some specific temperature threshold, and that simply does not happen. Dichotomizing continuous predictors is almost always a bad idea. You can read a lot about this here on CV by searching. Conversely, you could have nonlinear effects of temperature, which you could capture, e.g., by spline transformations.
You could probably do a lot of this in Excel, but something like R will be far better.