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Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system. [ABSTRACT FROM AUTHOR]

We explore the characteristics of 96,190 wet events (WEs) defined as consecutive 3‐hourly rainfall ≥ 1 mm/3 hr from a network of 34 stations across Bangladesh. Nearly 60% (5%) of WEs last ≤ 3 (≥ 15) hr. The WEs are dynamically clustered into four "canonical" storm types (STs), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. Approximately 60% of WEs are associated with ST#1, characterized by short and small WEs and very low rainfall amounts (usually <10 mm), ~30% of WEs are associated with either (ST#2) short/small WEs but with intense rainfall, probably mostly related to scattered thunderstorms or (ST#3) longer/larger WEs but with less intense rainfall. The last ST (ST#4) is rare (~6%), related to very long durations and large wet areas and includes the wettest WEs. It is especially frequent over southeastern Bangladesh. ST#2–ST#4 contribute almost equally to the local‐scale total amount of rainfall (27–29% each in mean) while ST#1, despite its individual low rainfall amount, still includes ~15% of it. ST#2 (ST#4) is related to the highest probability of occurrence of 3‐hourly (daily) extremes. ST#4 occurrence is the most impacted by synoptic Indian lows/depressions as well as the main modes of intra‐seasonal variation, while ST#1 and ST#2 are also significantly impacted by intra‐seasonal modes but in reverse manner than ST#4. [ABSTRACT FROM AUTHOR]