Below is a guide that could help you through the process:

When you have data covering the desired time and including daily rainfall amounts, perform a frequency analysis using statistical methods such as Log-Pearson Type III or Gumbel distribution. you can use statistical software or tools like Excel, R, or Python with libraries such as Numpy, Scipy, or Pandas.

Example using Python:

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import numpy as np

import scipy.stats as stats

# Assume `rainfall_data` is a list of annual maximum daily rainfall amounts

rainfall_data = [100, 120, 90, ...] # Replace with your data

# Fit the data to a Gumbel distribution

params = stats.gumbel_r.fit(rainfall_data)

# Calculate the 100-year return level (R0.01)

R0_01 = stats.gumbel_r.ppf(0.99, *params)

print(f"The R0.01 value is: {R0_01:.2f} mm")

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2. you can also calculate rainfall exceedance or what is called the probability that a certain rainfall amount will be exceeded in a given period.

Example using Python:

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# Assume `daily_rainfall` is a list of daily rainfall amounts

daily_rainfall = [5, 10, 0, ...] # Replace with your data

# Define the threshold for exceedance

threshold = 50 # mm

# Calculate exceedance probability

exceedance_probability = np.sum(np.array(daily_rainfall) > threshold) / len(daily_rainfall)

print(f"The exceedance probability for {threshold} mm is: {exceedance_probability:.2%}")

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You can use Gumbel distribution for daily values. Many software implement It

Thanks for the responses