Here is one idea, although I cannot say how applicable it will be.
In an idealized scenario, let us assume that in addition to weather time series you also have, for each individual component, a number for the time it was created, the time it was deployed, and the time it failed. Therefore you can compute the age and cumulative exposure of the component at failure.
In this idealized scenario, say we the weather data is $\mathbb{x}_t$, for $x=[x_1,\ldots,x_n]$ and $t=1,\ldots,T$ (e.g. $x_1=$wind speed, $x_2=$temperature, etc.). Then a reasonable approach might be to compute a time-integrated exposure as $X_{t+1}=X_t+x_t\Delta t$ (or you could use a midpoint rule, i.e. $t+\frac{1}{2}$). Then for component $i$ you have exposure $E_t=X_t-X_{\mathrm{deploy}}$ and age $t-t_{\mathrm{create}}$, which for simplicity we can append to the end of $E$ as another exposure factor.
So for component $i$ the predictor will be $E_t^{(i)}$, while the dependent variable will be "failed". This can be defined as a binary variable $F_t^{(i)}=(t>t_\mathrm{fail}^{(i)})$, i.e. 0 prior to failure and 1 after failure. Then you could combine all the component's data into a pair of big $E$ and $F$ arrays. A simple approach then would be to essentially treat this as a classification problem ("failed" vs. "not failed"), and apply logistic regression.
Now I will have to think about how this could work in your case, where you know only bulk failure rates (and perhaps deployment rates?) per day. I will update the answer if I come up with a solution.
In terms of survival models, I am really not familiar with them (beyond their existence and a few buzzwords). You would want to essentially model some sort of conditional hazard function, I guess, in terms of some "exposure adjusted age".