Are downpours getting more intense, even if the yearly total isn’t changing?

What you can answer

• Annual maximum 1-day and 5-day rainfall (Rx1day, Rx5day) per year
• Heavy-day contribution — R95p / R99p totals and their share of annual rain
• Simple daily intensity (SDII: mean rain per wet day)
• Trend and significance via Theil–Sen slope + Mann–Kendall test
• Intensity-vs-total decoupling — whether extremes rise while the annual sum stays flat

What you can NOT answer with these alone

• True absolute extreme magnitudes — IMERG biases the heaviest rates low; gauges or radar are needed for design-value rainfall.
• Sub-pixel cloudburst events — a localized cloudburst can sit inside one ~10 km pixel and be smoothed out; the half-hourly product and gauges resolve more.
• Cause attribution — these indices describe change, not whether it is anthropogenic vs natural variability (that needs detection-attribution methods).
• Short-record significance — ~25 years of IMERG limits power to detect weak trends; treat marginal Mann–Kendall results cautiously.

Code template

import earthaccess
import xarray as xr
import numpy as np
import pandas as pd
# pip install pymannkendall  (Theil–Sen slope + Mann–Kendall test)
import pymannkendall as mk

earthaccess.login(strategy="netrc")

# AOI: Mumbai & the Konkan coast
aoi = (72.6, 18.0, 73.6, 19.6)
years = range(2001, 2026)
wet_day = 1.0  # mm/day threshold defining a "wet day"

def annual_indices(daily):
    """daily: 1-D pandas Series of AOI-mean mm/day for one calendar year."""
    rx1 = daily.max()                                  # Rx1day
    rx5 = daily.rolling(5).sum().max()                 # Rx5day
    wet = daily[daily >= wet_day]
    sdii = wet.mean()                                  # simple daily intensity
    p95 = wet.quantile(0.95); p99 = wet.quantile(0.99) # baseline percentiles*
    r95p = wet[wet > p95].sum()                        # R95p total
    r99p = wet[wet > p99].sum()                        # R99p total
    return dict(Rx1day=rx1, Rx5day=rx5, SDII=sdii, R95p=r95p, R99p=r99p)
# *For strict ETCCDI, fix p95/p99 from a single baseline period, not per-year.

records = {}
for yr in years:
    granules = earthaccess.search_data(
        short_name="GPM_3IMERGDF",
        bounding_box=aoi,
        temporal=(f"{yr}-01-01", f"{yr}-12-31"),
    )
    # files = earthaccess.open(granules)
    # ds = xr.open_mfdataset(...)  # var: precipitation (mm/day in V07)
    # daily = ds.precipitation.sel(...).mean(dim=["lon","lat"]).to_series()
    # records[yr] = annual_indices(daily)

df = pd.DataFrame(records).T  # rows = years, cols = indices

# Trend test per index
for col in df.columns:
    res = mk.original_test(df[col].values)
    print(col, "slope/yr:", round(res.slope, 3),
          "p:", round(res.p, 3), "->", res.trend)

Expected output

• Per-year index table: Rx1day, Rx5day, SDII, R95p, R99p (with units stated)
• Trend lines: each index vs year with the Theil–Sen slope overlaid
• Trend summary: Mann–Kendall p-value and direction per index
• Decoupling panel: annual total (often flat) plotted against Rx1day / R99p (rising)
• Heavy-day share: % of annual rain from days above the 95th/99th percentile, per year

Caveats

• IMERG underestimates extremes — heaviest rain rates are biased low vs gauges, especially in convective monsoon downpours; cross-check CHIRPS / IMD gauges.
• Define every index — Rx1day, R95p, etc. are only interpretable with their definitions and the percentile baseline stated. Fix the baseline period for strict ETCCDI comparability.
• Western Ghats orographic bias — coastal/orographic rain along the Konkan is systematically under-sensed by satellite precipitation.
• Record length — ~25 years limits trend detectability; extend pre-2000 with TRMM only with care for inter-product inhomogeneity.
• V07 is current; V06 deprecated — reprocess cached pre-2023 data. IMERG Final has ~3.5-month latency; Late/Early trade accuracy for timeliness.

Cross-DAAC composition

GES DISC only for IMERG — single DAAC, single auth. CHIRPS (UCSB/CHC) and gauge data come from outside Earthdata and are optional cross-checks.

Sources

• GPM IMERG: https://gpm.nasa.gov/data/imerg
• IMERG V07 release notes: https://gpm.nasa.gov/sites/default/files/2023-07/IMERG_V07_ReleaseNotes_230713-signed.pdf
• ETCCDI extreme climate indices: https://etccdi.pacificclimate.org/list_27_indices.shtml
• CHIRPS: https://www.chc.ucsb.edu/data/chirps
• Mann–Kendall / Theil–Sen (pymannkendall): https://pypi.org/project/pymannkendall/