Extreme high-water estimates are needed for coastal flood-risk assessment and infrastructure design, but short, incomplete and strongly seasonal tidal-river records make them difficult to constrain. We analysed daily maximum water levels through 2025 at five Bangladesh Water Development Board stations in southwest coastal Bangladesh using a station-specific leave-one-year-out Monthly Climatological High-Water Reference (MCHR) derived from monthly means of daily maxima. Annual maxima were modelled with Normal, Gumbel-I and generalized extreme-value distributions fitted by L-moments (GEV-LM), while declustered peaks over threshold were analysed with a generalized Pareto distribution (POT-GPD). After 90 % annual completeness screening, 25–34 annual maxima remained per station. At the 100-year return period, the four stationary approaches differed by 15.6–21.5 %. POT estimates were stable across threshold and declustering choices at four stations, whereas SW244 showed an 18.75 % maximum deviation from its baseline estimate. Combined trend, change-point and likelihood evidence supported time-varying GEV location at four stations after false-discovery-rate adjustment and trend-free prewhitening sensitivity; likelihood evidence remained significant at all four, while SW243 remained stationary. Replacing the mean MCHR with a median reference changed 100-year GEV-LM and POT-GPD estimates by at most 2.79 % and 3.48 %, respectively. Cyclone-date checks showed that Aila, Amphan, Yaas and Remal coincided with annual maxima or unusually high anomalies at multiple stations. Extreme high-water estimates from short tidal-river records are most informative when model choice, sampling, non-stationarity, parameter uncertainty and data quality are reported alongside the point estimate instead of being collapsed into a single deterministic return level.