Meteorological data are likely to be interval. Mann-Kendal test if significant is an indication of the existence of a trend and is apt for such data. Therefore, the use of the test in this situation is a step in the right direction.

There is a variation of the Mann-Kendall that also has a seasonal component.  This may be useful.

Sometimes Sen's slope is used to determine the slope of the change in a time series.

There are other ways to decompose time series data into trend and seasonal components.

An example of these, using R, are here:

http://rcompanion.org/handbook/F_13.html

THANK YOU for your reply, can you please guide me about some reference material that I can read to understand all the process of how to do this?

It looks like XLStat conducts the Mann-Kendall test, with the option of including seasonality, and an option to calculate Sen's slope.  See the following:

https://www.xlstat.com/en/solutions/features/mann-kendall-trend-tests

https://help.xlstat.com/customer/en/portal/articles/2062303

This discusses the seasonal Mann Kendall:

http://www.stats.uwo.ca/faculty/aim/1994Book/1994-Time-chapter%2023.pdf

Reference is:

http://www.stats.uwo.ca/faculty/aim/1994Book/

Trust me,

You don't have to be an expert in statistics, Read

DETECTING TREND AND OTHER CHANGES IN HYDROLOGICAL DATA by Zbigniew W. Kundzewicz and Alice Robson

Statistical Methods for Environmental Pollution Monitoring by Richard O. Gilbert

Refer the code available in my to perform Different versions of Mann-Kendall test.

Consider using the R package modifiedmk to perform trend tests