Ways to simulate weather data over several periods (Python or R)? I have a time series dataset that has several variables for a state/province for fixed periods of time. That is for state A, there are samples from April 2017 to July 2019. Of course, I thought adding precipitation and temperature variables would be a great idea. I tried finding some relevant external data but most of it is abstract and spread out. How would one simulate dynamic data in Python with varying means, highs and lows for say six months on a daily basis, taking into account average temperatures/precipitation for each month?

So if I have the mean temperatures (C) as below for state A:

year    Jan   Feb   Mar   Apr   May   Jun
2017    5.5   6.0   12.0  15.0  20.0  25.0


I would like data to be simulated as below without really doing for each month since that would make the whole task very tedious:

Duration     Temp
2017-01-01   5.0
2017-01-02   5.1
2017-01-03   4.9
.
2017-03-01   7.8
2017-03-02   9.0
2017-03-03   9.5
.
2017-06-30   26.7


Are there ways to achieve this in Python (or R)?
What about creating a Pandas DataFrame and adding a new column such as "Temp_simulated" and simulate the temperature?