On main (0f6631a, pandas 3.1.0), both of these worked before #2924 and now raise:

import pandas as pd
import pvlib

loc = pvlib.location.Location(49.28, -123.12, tz='America/Vancouver')
times = pd.date_range('2026-03-07', '2026-03-10', freq='1min',
                      tz=loc.tz, inclusive='left')
ghi = loc.get_clearsky(times)['ghi']
pvlib.clearsky.detect_clearsky(ghi, ghi)
# NotImplementedError: algorithm does not yet support unequal time intervals...

rain = pd.Series(0.0, index=pd.date_range('2026-01-01', periods=720, freq='h'))
pvlib.soiling.kimber(rain)
# TypeError: Cannot convert tz-naive timestamps, use tz_localize to localize

In _get_sample_intervals, tz_localize(None) turns the index into local clock time, so the 1-minute data gets a 61-minute step where the clocks change. temperature.prilliman uses the same helper, so it fails the same way.

kimber uses tz_convert(None), which raises on a tz-naive index:

What I'd suggest: subtract the timestamps directly. That gives elapsed time for both naive and tz-aware indexes.

# tools._get_sample_intervals
deltas = (times[1:] - times[:-1]) / pd.Timedelta('60s')

# soiling.kimber
timestep_interval = rainfall.index[1] - rainfall.index[0]
day_fraction = timestep_interval / pd.Timedelta(hours=24)

Plus two tests: detect_clearsky on a DST day should match the same data in UTC, and kimber with a tz-naive index should match the existing expected output.

I tried this locally:

• both new tests fail on main and pass with the change;
• test_clearsky.py, test_soiling.py, test_temperature.py and test_tools.py pass on pandas 3.1.0 and 2.3.3;
• on the commit before [DEV] address pandas 3.1.0 compatibility issues #2924 (d6075f6), both examples run fine.

I didn't find an existing issue for this. Happy to open a PR if this sounds right.

AI disclosure: I used Claude Code (an AI coding agent) to find and trace this, run the examples and tests on my machine, and draft this note.

@echedey-ls @kandersolar