carMonthly = pd.DataFrame(data={'avSpeed': [40.3, 23.4], 'dist': [100, 150]},
                       index=pd.PeriodIndex(['2019-02', '2019-05'], freq='M'))

         avSpeed  dist
2019-02     40.3   100
2019-05     23.4   150

avSpeed      dist
2019-02-01     40.3  3.571428
2019-02-02     40.3  3.571428
...
2019-02-27     40.3  3.571428
2019-02-28     40.3  3.571428  #until end of February
2019-05-01     23.4  4.838710
2019-05-02     23.4  4.838710
...
2019-05-30     23.4  4.838710
2019-05-31     23.4  4.838710

carDaily = pd.DataFrame()
carDaily['avSpeed'] = carMonthly['avSpeed'].resample('D').ffill()
tempSeries = carMonthly['dist'].resample('D').first()
carDaily['dist'] = tempSeries.groupby(tempSeries.notna().cumsum())
    .apply(lambda x: x/len(x.index)).ffill()

            avSpeed      dist
2019-02-01     40.3  1.123596
2019-02-02     40.3  1.123596
...
2019-04-29     40.3  1.123596
2019-04-30     40.3  1.123596  #until end of April
2019-05-01     23.4  4.838710
2019-05-02     23.4  4.838710
...
2019-05-30     23.4  4.838710
2019-05-31     23.4  4.838710