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Article

Assessing Climate Trends in Bangladesh Using the Spatial Synoptic Classification

by
Nishat T. Sumaya
1,
Jason C. Senkbeil
1,* and
Scott C. Sheridan
2
1
Department of Geography and the Environment, The University of Alabama, Tuscaloosa, AL 35401, USA
2
Department of Geography, Kent State University, Kent, OH 44242, USA
*
Author to whom correspondence should be addressed.
Climate 2025, 13(11), 222; https://doi.org/10.3390/cli13110222
Submission received: 25 September 2025 / Revised: 18 October 2025 / Accepted: 24 October 2025 / Published: 27 October 2025
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Abstract

Climate change is reshaping weather patterns and atmospheric circulation globally, particularly in monsoon-dominated tropical environments. To examine how these changes are unfolding in Bangladesh, we extend the Spatial Synoptic Classification (SSC) using ERA5 reanalysis (1960–2024) at three representative stations (Chittagong, Khulna, and Sylhet) to assess long-term changes in the SSC weather types and their internal meteorological properties. The SSC calendars were constructed and analyzed for seasonal distribution, interannual trends, and decadal anomalies of temperature and dew point. Results reveal that Bangladesh’s climatology is dominated by Moist Tropical (MT), Moist Moderate (MM), and Dry Moderate (DM) weather types with a coherent seasonal cycle. Interannually, MT increased strongly across all stations, while MM and DM declined significantly. Decadal anomalies show consistent warming and moistening since the 2000s, which are most pronounced for Dry Tropical (DT) and MT. These findings indicate that climate change in Bangladesh is expressed not only through shifting frequencies but also through evolving thermodynamic characteristics of daily weather types, underscoring the SSC framework’s value in tropical monsoon regions for generating actionable climate information to support heat-stress planning and climate-health services.

1. Introduction

Climate change is reshaping weather patterns and large-scale circulation globally [1,2]. However, the response is not even, as synoptic and regional-scale trends do not always mirror the global trend [3,4]. These changes are especially seen in tropical regions. Amplified Arctic warming and relative Southern Ocean cooling enhance northward ocean heat transport, shift the ITCZ toward the warmer Northern Hemisphere, and alter cross-equatorial Hadley circulation [5,6]. Tropical monsoon regions are already registering these changes, since increasing interhemispheric temperature contrasts are implicated in tropical rain-belt migration and evolving monsoon precipitation patterns [7,8].
Climate-induced risk is also uneven because it emerges from interactions among physical, ecological, and social systems [9]. In particular, South Asia is widely recognized as the most vulnerable region globally to climate change impacts [10]. Bangladesh is a South Asian monsoon-dominated country often characterized as a climate change “hotspot” due to its location at the intersection of more temperate subtropical and semi-humid climates, which makes it vulnerable to shifts in general circulation patterns [11,12]. The country also faces a wide range of hydro-climatic hazards including cyclones, floods, storm surges, erratic rainfall, and drought that pose recurrent risks to people and infrastructure [13,14,15]. Concerns that the frequency and severity of such events are increasing have become a persistent policy issue. In this context, climate-sensitive sectors such as agriculture and fisheries may face growing challenges from increasing unpredictability in weather types and the shifting seasons [16,17]. Crucially, small changes in the mean and/or consistency of climate variables can disproportionately increase the probability of extremes [18]. This amplifies risks to crops, infrastructure, and health systems where planning horizons depend on reliable climate information.
Most national studies in Bangladesh emphasize the temporal evolution of individual variables, with particular attention paid to temperature and monsoon precipitation [19,20,21,22,23,24,25]. In parallel, Sultana et al. [17] report a spatially uneven scholarly focus skewed toward the southwest, leaving the cyclone-exposed southeast coast and the biodiversity-rich northeastern hill–wetland systems comparatively underrepresented, despite their susceptibility to climate impacts. Moreover, while these efforts are informative, to our knowledge, no study has examined long-term climatological trends in daily weather types for Bangladesh that capture how groups of atmospheric variables evolve together in the face of climate change.
The Spatial Synoptic Classification (SSC) is a robust method in synoptic climatology. The SSC is a widely used, airmass-based weather-type scheme that assigns each day to a specific weather type using surface meteorological data (e.g., diurnal temperature and dew point behavior, wind data, mean sea-level pressure and cloud cover) [26]. The SSC retains the joint behavior of atmospheric variables, which is closer to how organisms and human systems experience stress [27]. The SSC was first developed and applied in mid-latitude settings such as North America where the atmospheric circulation is mainly defined by synoptic-scale events; however, its recent applications in the tropics (e.g., Kenya) underscore its adaptability to diverse regimes [28]. Although tropical monsoon climates are often shaped by mesoscale convective systems (MCSs) that evolve somewhat independently of classic synoptic patterns [29], the SSC can still capture tropical variability. Therefore, in this research, we aim to extend the application of the SSC to the South Asian monsoon country of Bangladesh to retain meaningful multivariate information where both synoptic and mesoscale processes contribute to the climate.
The SSC method has also demonstrated public-health utility in the identification of localized heat-stress thresholds and to underpin heat-stress warning systems with responses tied to particular weather types [26,30,31]. This seems pertinent in Bangladesh, where warming and heat-related morbidity are ongoing concerns [12,32], and where climatic factors have been linked to vector-borne and enteric disease risks, including dengue and gastrointestinal illness [33,34]. Together, these applications suggest future potential for SSC outputs in climate-health services.
Therefore, building on synoptic climatology, this research develops an SSC scheme for three stations, Chittagong, Khulna and Sylhet in Bangladesh, and uses it to illustrate the distribution and patterns of daily weather types across the country with attention paid to their temporal behavior. Specifically, we ask the following questions: (1) What is the distribution of SSC weather types for Bangladesh? (2) Are there annual trends in SSC weather-type frequencies? (3) How do changes in SSC weather-type frequencies and their internal climatological characteristics help to explain long-term changes in Bangladesh’s climate? This work addresses a clear methodological gap and lays a practical foundation for downstream applications from agricultural advisories and heat-stress planning to early signals relevant for climate-sensitive disease management within Bangladesh’s monsoon-dominated environment.

2. Background

2.1. Climate of Bangladesh

Bangladesh, located between 20°34′ N–26°38′ N and 88°01′ E–92°41′ E, is the world’s largest deltaic plain, situated within the Ganges–Brahmaputra–Meghna (GBM) river basin [23]. With a total area of 147,700 km2, the country extends approximately 820 km north–south and 600 km east–west [35]. About 80% of its landmass is composed of low-lying floodplains with elevations generally between 1 and 60 m above mean sea level, while hilly terrains are confined to parts of the northeast and southeast [36]. The tropical monsoon climate of the country is strongly modulated by Himalayan topography to the north, the GBM river network across the country, and the Bay of Bengal to the south, which together influence the intensity and spatial expression of the southwesterly summer monsoon.
The annual climate cycle comprises a hot–humid pre-monsoon (March–May/MAM), a hot and wet monsoon (June–September/JJAS), a drier post-monsoon (October–November), and a cool, dry winter (December–February/DJF) [36,37,38]. Despite local physiographic contrasts, regional climatic gradients remain modest due to the broad low-relief Bengal Basin, the shielding effects of surrounding hill chains, and the moderating influence of abundant surface water bodies [39,40]. In Koppen terms, Bangladesh is predominantly tropical (Af/Am) with a temperate (Cwa) fringe in the north [40], a pattern that frames the mixture of moist and dry air expected over the country. Climatological means (1991–2020) reflect this warm, humid monsoon climate [41]. The mean annual temperature is 25.71 °C, with June typically the hottest month (average temperatures range between 25.49 and 32.01 °C) and January the coolest (~12.31–25.26 °C). Annual precipitation averages 2174.10 mm, peaking in July (478.48 mm on average) and reaching a minimum in December (5.44 mm). Relative humidity remains high year-round, averaging 80.56% (over 1990–2020) with a lowest monthly mean in April (51%), while the highest is observed in July (94.52%) [42].
A growing body of evidence indicates a shifting baseline in the climate of Bangladesh. Multi-decadal analyses show a sustained warming trend across Bangladesh [21] and escalating heat stress, with summer heat indices frequently reaching 42–50 °C [12,43]. Mallick et al. [24] report notable warming trends in Bangladesh, reflected by increases in several warm temperature indices since the 2000s, specifically daily temperature range (DTR), annual count of summer days (SU), and monthly maximum daily minimum temperature (TNX). In contrast, several cold-temperature indices declined after the 2010s, including the minimum value of daily minimum temperature (TNN) and minimum value of daily maximum temperature (TXN). Changes in precipitation characteristics are also evident. The frequency of precipitation days has increased while precipitation intensity has shown rapid shifts since the 2000s and 2010s [23,44]. Precipitation concentration also exhibits upward tendencies in most of the regions, particularly in the central and southeast region [23]. Taken together, these signals indicate changing weather patterns in Bangladesh. Evaluating these shifts with a synoptic climatology-based method that classifies the observed conditions into recurring weather types is well-motivated.
Concurrently, located just above the tropics and north of the Bay of Bengal, Bangladesh is prone to the rapid onset of meteorological hazards. Climate change intensifies these natural phenomena in terms of severity and frequency, resulting in hydro-meteorological extremes including cyclones, floods, storm surges, erratic rainfall, and drought [14,45,46]. Bangladesh ranks seventh among the most at-risk countries around the world by weather-related loss events [47]. In the face of the changing climate and being an agriculture-dependent country, Bangladesh is at a heightened need for timely, decision-relevant climate information for risk-aware planning and human safety [39,40].

2.2. The Spatial Synoptic Classification (SSC)

The Spatial Synoptic Classification (SSC) is an automated method that assigns each day’s observed surface weather to one of seven weather types using multiple atmospheric variables (temperature, dew point, wind speed, wind direction, mean sea-level pressure and cloud cover). The details of the generic descriptions of SSC weather types can be found on the SSC website (http://sheridan.geog.kent.edu/ssc.html, accessed on 10 August 2025) and are briefly discussed below. The SSC nomenclature characterizes the weather experienced at a particular location, not the remote source region of a weather type. Because the SSC is a station-specific approach, the same synoptic disturbance may map to different types at nearby locations, as mesoscale settings and local terrain modulate the surface signature of atmospheric variables [28]. In monsoon environments, where land–sea interactions and convective organization imprint noticeable spatial gradients, this sensitivity is advantageous, hypothetically capturing the heterogeneity in realized surface conditions. Thus, the refinement and modification of the SSC scheme for the tropics are necessary to represent the distinctive behavior of each regional climate [48]. The following generic SSC weather-type descriptions were developed for the mid-latitudes. These descriptions can be altered for specific regions in the tropics after carefully studying the relationships with other climatic factors and atmospheric circulation specific to the region. It is not the intent of this research to write descriptions for Bangladesh because that understanding has not been developed or established yet and will be the subject of future research.
Dry Polar (DP): Typically, the coolest and driest days with clear skies.
Dry Moderate (DM): Mild and dry; often reflects a modified state of another type as thermal moisture characteristics of the weather types shift away from the origins [49]; episodes may coincide with relatively zonal flow aloft.
Dry Tropical (DT): Hottest and driest conditions; strong surface heating with depressed dew points; subsidence/downslope influences possible; convection commonly inhibited; typical of heat-episode environments.
Moist Polar (MP): Cool, cloudy and humid; frequently linked to inland transport of maritime air.
Moist Moderate (MM): Mild, cloudy, and humid; commonly a modified MT environment where extensive clouds cap daytime heating.
Moist Tropical (MT): Warm to hot and very humid convective environments lacking strongly organized kinematic structure; convective precipitation is common.
Transitional (TR): Days best characterized as a shift from one weather type to another.

3. Data and Methods

3.1. Study Area

For this study, three representative stations were selected across distinct physiographic and climatic settings to develop the SSC (Figure 1 and Table 1). These are Chittagong on the open southeast coast and adjacent hill tracts, Khulna on the southwest deltaic plain adjacent to the Sundarbans and Sylhet in the northeast foothills, where orographic enhancement yields the nation’s wettest climate.
Given Bangladesh’s relatively small land area and modest topographic variation, producing the SSC for many stations that are both at geographical proximity and homogenous climate zones would be redundant, as adjacent sites often show cohesive patterns of the SSC weather types [26]. The chosen stations span the country’s three dominant Koppen climate zones and key physiographic domains, thereby maximizing climatological representativeness while maintaining methodological and analytical efficiency. Three stations for northwestern Bangladesh were used in trials but all had suspicious data inconsistencies that were anomalous and incompatible. Therefore, reliability was preserved by careful analysis and quality control of the data at the three stations that were selected.

3.2. Data Analysis

3.2.1. Creating the SSC for Bangladesh

To develop the SSC for Bangladesh, the ERA5 hourly single-level reanalysis from the European Centre for Medium-Range Weather Forecasts was obtained for 1960–2024. Six-hourly (0300, 0900, 1500, and 2100 UTC+6) meteorological variables, including 2 m temperature, 2 m dew-point temperature, 10 m u-component (east–west) and v-component (north–south) wind, mean sea-level pressure, and cloud cover, were extracted for the domain 20.5–27.0° N, 87.5–92.5° E. To align with the SSC’s station-based methodology, grid cells within this domain were converted to station-centric time series. The grid cells nearest the Chittagong, Khulna, and Sylhet meteorological stations were retained as the three SSC stations in Bangladesh. The relatively high temporal and spatial resolution (0.25° latitude × 0.25° longitude) of ERA5 is expected to provide a more detailed representation of regional climatology than coarser reanalysis products. Moreover, ERA5 has been reported to represent synoptic patterns with reasonable consistency in the satellite era [50].
Following the SSC framework, each station’s daily surface conditions were classified into one of six weather types: DP, DM, DT, MP, MM, MT or a Transitional (TR) category [26]. The process works by identifying seed days, i.e., dates that best represent the typical surface signature of each type at that location. In SSC version 3 (SSC3), seed-day selection was automated within four two-week windows spaced through the year, after which smooth “sliding seed-day” curves were constructed to describe the seasonal evolution of each weather type’s characteristics at daily resolution, thereby allowing the determination of day-specific theoretical seed-day criteria [26,48].
Once the seed-day criteria were determined for each day of the study period, SSC weather types were assigned by comparing the observed surface meteorological conditions with the theoretical seed-day values for all candidate types. The hk error score (an equally weighted sum of squared standardized departures) was computed for each type, and the day was assigned to the class with the lowest score [26]. A post-check for Transitional conditions was then applied; if the Transitional error score was lower than the other types, the day was relabeled as TR. The outcome is a station-specific SSC calendar for 1960–2024 at each site, available at http://sheridan.geog.kent.edu/ssc3.html (accessed on 10 August 2025) with the station codes BD1, BD2 and BD3 (corresponding to Chittagong, Khulna, and Sylhet, respectively).

3.2.2. Statistical Analysis

From the SSC calendars, seasonal and annual frequencies (percent of days) for each weather type were computed for 1960–2024. Interannual trends in type frequency were assessed at each station using ordinary least squares (OLS) regression on annual percentages. Where normal distributional assumptions were not satisfied or the residuals of the OLS regression exhibited autocorrelation, a Modified Mann–Kendall (MMK) test with Hamed–Rao variance correction was applied in parallel with Sen’s slope for estimating trend magnitude. This non-parametric test is comparatively insensitive to an abrupt shift, particularly in hydro-climatic records [51], and provides robust trend detection under autocorrelation [52]. Slopes are reported as percentage points per decade, accompanied by 95% Confidence Intervals (CIs), and statistical significance is evaluated at α = 0.05. Together, these procedures provided a consistent, station-relative depiction of how the distribution and temporal behavior of daily weather types have evolved, supporting comparison across stations in Bangladesh.

4. Results and Discussion

4.1. SSC Weather Types in Bangladesh

Across all three SSC stations, MT, MM, and DM weather types dominate the climatological frequency distribution, whereas the polar types (DP, MP) are negligible over 1960–2024 (Table 2; total values reflect the dominant weather types in Bangladesh calculated as long-term mean frequencies of occurrence). MT is the leading weather type at every station, averaging 48.5%. MM generally ranks second (22.4% on average), except at Chittagong where DM is comparatively stronger. DM remains climatologically important across the other two stations, averaging 18.7% across stations.
The SSC successfully confirms Bangladesh’s seasonal cycle, as visualized in Figure 2 and quantified in Table 2. Winter (DJF) is dominated by DM at all stations (41.8–55.2% on average), reflecting cool, dry continental influence and weak moisture supply. DM was relatively high at the southern stations (between 43.11 and 63.16%), and slightly lower in Sylhet (33.1–46.8%). During this season, MT remains the principal moist weather type; however, its role is diminished (averaging between 29.1 and 34.3%).
Moving into the pre-monsoon (MAM), DM declines sharply from an average 41.8% in February to 18.1% in March, nearly disappearing by May as heating intensifies. In response, DT emerges most clearly in MAM, peaking in April (11.3% on average), reflecting the hot, comparatively drier episodes typical of the pre-monsoon season when national maximum temperatures also become highest [21,39,41]. In parallel, MT weather types in-crease steeply, with cross-station frequencies averaging 65.4% by April, particularly in Sylhet where the highest MT frequency is observed (77.2% in April). This regional spike may be associated with pre-monsoon thunderstorms (Ts)/ Nor’westers (locally known as Kalbaishakhi), which are intense mesoscale convective systems that typically form over northwestern and eastern Bangladesh and move southeastward, often bringing heavy rainfall, gusty winds, and lightning [53,54]. Evidence suggests that Sylhet, located in the northeast, experiences the highest frequency of Ts, while southeastern regions like Chittagong experience fewer [55,56,57]. Since MT types often capture moist convective episodes in SSC [58], this may partly explain the elevated MT frequencies observed in Sylhet.
By May, the combined share of dry types is minimal, signaling the imminent transition to the monsoon. During the monsoon (JJAS), moist types prevail across the stations. MM frequencies escalate sharply, from 20.7% in May to 38.7% in June, peaking in July at 44.7%. While MT gradually declines across the season with the increased cloud cover, the cross-station average of MT frequencies remains dominant overall. A regional contrast emerges at Sylhet, where MM overtakes MT in July–August, exceeding 52%, while MT falls below 43%. This regional anomaly reflects Sylhet’s orographic positioning near the Himalayas and Meghalaya Plateau and its embeddedness in the monsoon trough. The Bay of Bengal acts as a moisture source, and the intensified land–sea thermal contrast drives low-level jet (LLJ) activity, which channels this moisture inland. Orographic lifting along mountain slopes further amplifies precipitation via vertical moisture flux convergence, reinforcing MM dominance in northeastern Bangladesh [59,60]. The pattern aligns with Sylhet’s climatology as the wettest region in Bangladesh with highest annual precipitation around 4126.16 mm [23,39,41].
The post-monsoon period (October–November) marks a rapid atmospheric adjustment as the monsoon moisture supply retreats. MM types decline sharply, dropping from an average of 37.9% in September to 25.9% in October, signaling the weakening of widespread moisture convergence. Simultaneously, MT types begin to rise gradually after September before declining in November. Tropical Cyclone (TC) activity is very common in Bangladesh during this post-monsoon season. Historical records from 1960 in 2023 showed that nearly half (48.57%) of all TCs impacting Bangladesh occurred during October-November [61], with the southwest Khulna and southeast Chittagong stations most affected [62]. As the atmosphere progresses toward winter, relative humidity and moisture content decrease significantly [63]. Correspondingly, DM weather types rebound, rising sharply from 10.5% in October to 35.1% in November, reflecting the seasonal reversal of wind patterns and the dominance of dry continental air. This transition effectively closes the loop on the synoptic annual cycle.
Taken together, these results depict a coherent seasonal progression common to all stations: DM-dominated winter, thermally enhanced pre-monsoon with a brief DT signal and surging MT, monsoon with pervasive MM and MT, post-monsoon drying and DM recovery overlaid by meaningful regional structure. Chittagong exhibits the strongest DM in winter, Sylhet shows the most pronounced mid-monsoon MM prominence, and Khulna sits between these end members. Moreover, southern coastal stations show persistently high MT in April–May. The SSC framework thus provides a multivariate view of lived daily weather in Bangladesh, tightly aligned with the country’s monsoon-dominated climatology.

4.2. Trends in SSC Weather Types

4.2.1. Interannual Variability

The long-term mean frequencies of SSC weather types at all three stations points to an evolution of the daily weather regime toward MT conditions and away from cooler/drier and moderate types (Figure 3 and Table 3). MT increases are large, monotonic, and statistically robust, while MM and DM decline and rarer classes (DP, MP, TR) are also trending downward.
MT is the dominant signal with an increasing trend exceeding 5% per decade (p < 0.001) at every site. Across stations, it rises from 30–45% in 1960 to 63–65% by 2024 with r2 greater than 0.70, indicating both the strength and persistence of the trend. Durbin–Watson statistics for Chittagong, Khulna, and Sylhet showed that no autocorrelation was present. MT has now become the most dominant weather type across Bangladesh, reflecting the intensification of moist and warmer conditions.
In contrast to the MT increase, MM and DM declined significantly at all stations. Positive autocorrelation was present at all three stations for MM and DM. MM exhibited the steepest retreat, decreasing at rates of 3.65%, 2.75%, and 4.32% per decade in Chittagong, Khulna, and Sylhet, respectively (all p < 0.001). Over the record, MM fell from 25.21% to 9.42% in Chittagong, 23.84% to 11.36% in Khulna, and 32.05% to 6.65% in Sylhet. DM also declined, though more moderately, at 1.34–1.82% per decade (p < 0.001) across stations. Overall, these changes indicate a shift of the moderate days that were once an integral part of the seasonal cycle to thermally stronger tropical days.
For the rarer weather types, small but generally downward tendencies were evident. Both DP and MP exhibited statistically significant declines (p < 0.05) across all stations; however, the magnitude, particularly for DP, was negligible. TR days also showed statistically significant but small declining trends only at southern stations. DT was more variable, with non-significant increases in Chittagong and Khulna, but a significant rise in Sylhet (0.42% per decade, p = 0.042), suggesting a growing frequency of hot, dry episodes in the northeast relative to the early baseline.
Overall, these trends demonstrate that the SSC climatology of Bangladesh is evolving toward greater dominance of MT conditions, a decline in moderate types, and localized signals of intensifying heat. The consistent agreement in direction and magnitude of change across stations reinforces the robustness of the SSC methodology in monsoonal contexts, offering compelling evidence of a changing climatology.

4.2.2. Changes in Weather Types over Time

The interannual trend analysis revealed the evolution of daily weather types across the country. The SSC climatology of Bangladesh is shifting toward warmer and more humid regimes, driven by the increasing dominance of MT days and the retreat of drier and moderate classes. Beyond frequencies alone, it is also critical to examine how the meteorological conditions within these weather types are evolving. To address this, decadal anomalies of temperature and dew point temperature are analyzed for the dominant types (DM, MM and MT), alongside DT, which is less frequent but exhibits a seasonally distinctive presence in Bangladesh’s pre-monsoon season. All anomalies are expressed relative to the 1960–1999 climatological baseline (Figure 4).
Historically associated with Bangladesh’s winter climatology, DM days have typically featured mild temperatures, low humidity, and suppressed convective activity under the influence of dry continental air. Since the 2000s, however, the thermal character of DM has shown a consistent warming trend across all stations. By the 2020s, decadal temperature anomalies reached 1.6 °C in Chittagong, 1.1 °C in Khulna, and 1.4 °C in Sylhet, levels significantly elevated from the near-baseline conditions that prevailed throughout the 20th century. Dew point anomalies also rose markedly between the 2000s and 2010s, increasing from −0.1 °C to 0.6 °C, –0.3 °C to 0.2 °C and −0.1 °C to 1.1 °C in Chittagong, Khulna, and Sylhet, respectively. This suggests that DM days were becoming less “dry” in character between the 2000s and 2010s; however, this moistening was not consistent, since dew point anomalies partially retreated toward the baseline by the 2020s. Thus, the overall signal indicated that warming was the more persistent signal within DM, whereas moistening was episodic.
The MM type exhibited more irregular and spatially heterogenous behavior in temperature and dew point anomalies. Temperature anomalies fluctuated across decades with no strong persistent trend across stations. In Chittagong and Khulna, temperature anomalies declined slightly in the 2020s (−0.3 °C), whereas Sylhet showed a modest warming to 0.6 °C. Dew point anomalies again diverged, with Chittagong and Khulna experiencing slight drying (−0.2 °C), while Sylhet continues to moisten (0.7 °C). This variability indicates that MM days are less systematically transformed than DT or MT, but they still reflect the broader moistening of Bangladesh’s climate, while the spatial differences may reflect weakening synoptic coherence during the monsoon onset and withdrawal phase.
In contrast, the DT days most oppressive during the pre-monsoon exhibited the strongest and most consistent intensification. Decadal temperature anomalies rose sharply after the 2000s, reaching 3.5 °C in Chittagong, 3.4 °C in Khulna, and 2.6 °C in Sylhet by the 2020s. This warming was accompanied by a sharp increase in dew point anomalies. Between the 2000s and 2020s, DT dew point anomalies surged from 1.5 °C to 4.3 °C in Chittagong, 1.1 °C to 4.9 °C in Khulna, and 1.3 °C to 6.0 °C in Sylhet. Such combined warming and moistening suggests that DT days are intensifying in both sensible and latent heat. Such hybridization of hot–humid conditions, especially during the pre-monsoon, greatly elevates heat-stress risk and begins to blur the boundaries between DT and MT. The intensification of DT is especially noteworthy in Sylhet, where historically moderate temperatures due to elevation and wetlands are now giving way to highly oppressive conditions, possibly driven by enhanced convective activity and altered land–atmosphere interactions.
MT, the climatologically dominant SSC type in Bangladesh, showed a trajectory similar to DT with a consistent post-2000 rise in both temperature and dew point anomalies. By the 2020s, temperature anomalies had reached 0.6 °C in Chittagong, 0.7 °C in Khulna, and a notably higher 1.7 °C in Sylhet. Dew point anomalies followed a similar pattern, with Sylhet again leading (2.3 °C), followed by Chittagong (0.8 °C) and Khulna (0.4 °C). This dual warming–moistening trend reinforces MT’s climatological dominance and reflects how the background climate is shifting toward a hot and humid atmosphere that sustains longer and more intense monsoon-like conditions. The magnitude of change in Sylhet points to enhanced moisture availability, stronger convective potential, and elevated latent heat flux, possibly driven by increased low-level moisture transport from the Bay of Bengal and intensified low-level jet activity.
Collectively, these decadal anomalies reveal that the climatology of SSC weather types in Bangladesh is undergoing not only a significant change in frequency but also a fundamental transformation in thermodynamic character. The evidence presented here demonstrates that these climatic changes are not abstract projections but observable, measurable departures from historical norms. When extended with decadal anomaly diagnostics, the SSC framework offers a useful lens to detect and interpret these changes, capturing how the mean thermal and moisture characteristics of each weather type have drifted over time.

5. Conclusions

This study extended the Spatial Synoptic Classification (SSC) to Bangladesh using ERA5 data (1960–2024) for three representative stations (Chittagong, Khulna, and Sylhet) to quantify how daily weather types and their meteorological character were changing in a monsoon-dominated climate. We examined both the distribution of SSC weather types and their long-term evolution, combining frequency trends with decadal anomalies of temperature and dew point to capture shifts in occurrence and in the internal properties of each type.
In Bangladesh, the SSC climatology was dominated by MT, MM, and DM, whereas DP and MP occurred only rarely. The annual cycle was well resolved and regionally coherent across the three stations. Winter (DJF) was characterized by DM. During the pre-monsoon (MAM), DM declined rapidly while a brief but distinctive DT signal appeared and MT surged. During the monsoon (JJAS), moist weather types dominated across Bangladesh, with a pronounced rise in MM during July–August. In Sylhet, which is the wettest region of the country, the SSC reproduced the regional monsoon signal: at peak season, MM overtook MT. This crossover suggested orographic lifting and proximity to the monsoon trough, with enhanced moisture convergence, cloud cover, and moderated heating, conditions that together favored MM dominance there. The post-monsoon brought rapid adjustment: MM declined, MT showed a transient rise, and DM started to recover. These patterns aligned with Bangladesh’s seasonal cycle and revealed meaningful spatial structure across the three stations.
Long-term changes showed a pronounced reorganization toward warmer, more humid regimes. MT increases were large and persistent at all stations (exceeding 5% points per decade), while moderate types (MM and DM) declined significantly. Rarer types (DP, MP) and TR also showed negligible to small declines across stations, while DT was variable overall but increased significantly in Sylhet, indicating growing pre-monsoon heat episodes in the northeast. The internal thermodynamics of the types also drifted. All four analyzed types (DM, MM, DT, MT) exhibited some level of thermal intensification after the 2000s, though the pace and profile of change varied by type and region. DT and MT days warmed and moistened simultaneously. DM days, while still cooler and drier than their moist counterparts, have faced warming since the 2000s. MM days showed less systematic evolution, but their variability reflected the moistening of Bangladesh’s climate. These results underscored that the climatological transition toward warmer and more humid weather types was embedded in both the frequency and the thermodynamic properties of the SSC types.
Overall, the results of this research agree with previous research that climate change is reshaping Bangladesh’s climatology, evidenced by long-term changes in both the frequency and the internal meteorological character of the SSC weather types: more MT, fewer MM/DM, and internal warming and moistening across several types, particularly after the 2000s. These observable shifts are consistent with the understanding that even 1–2 °C increases in mean temperature can disrupt climatological norms and substantially alter daily weather, effects widely recognized as climate change impacts [64]. By retaining the joint behavior of atmospheric variables, the SSC framework proves to be a robust diagnostic tool for observing climate change in the monsoon-dominated country as it is expressed in the daily atmosphere, rather than in isolated single-variable trends. The findings have direct utility for decision-making, supporting heat-stress planning, agricultural advisories, and climate-health services. Future work will examine intensity subsets (e.g., DT+, MT++) and couple SSC variability with middle- and upper-level circulation and moisture transport to strengthen early-warning and climate-service applications.

Author Contributions

Conceptualization: N.T.S. and J.C.S.; methodology: S.C.S., J.C.S. and N.T.S.; formal analysis: N.T.S., S.C.S. and J.C.S.; writing—original draft: N.T.S. and J.C.S.; writing—review and editing: J.C.S., N.T.S. and S.C.S.; visualization: N.T.S.; supervision: J.C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

ERA5 hourly single-level data were obtained from the Copernicus Climate Data Store (ECMWF): https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview (accessed on 10 August 2025). Station-specific SSC calendars for Bangladesh are available at the SSC website http://sheridan.geog.kent.edu/ssc3.html (accessed on 10 August 2025) under codes BD1, BD2, and BD3 (Chittagong, Khulna, and Sylhet, respectively).

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. Map of study area showing the three SSC stations in Bangladesh (Chittagong, Khulna and Sylhet).
Figure 1. Map of study area showing the three SSC stations in Bangladesh (Chittagong, Khulna and Sylhet).
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Figure 2. Mean monthly distribution of SSC weather-type frequencies at Chittagong (a), Khulna (b), and Sylhet (c) (1960–2024).
Figure 2. Mean monthly distribution of SSC weather-type frequencies at Chittagong (a), Khulna (b), and Sylhet (c) (1960–2024).
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Figure 3. Mean annual change in SSC weather types at study SSC stations (1960–2024). R2 values are reported for trends with OLS, while p-values are shown for trends assessed via the MMK test. Solid lines indicate statistically significant trends (p < 0.05), while dashed lines (---) represent non-significant trends (p ≥ 0.05).
Figure 3. Mean annual change in SSC weather types at study SSC stations (1960–2024). R2 values are reported for trends with OLS, while p-values are shown for trends assessed via the MMK test. Solid lines indicate statistically significant trends (p < 0.05), while dashed lines (---) represent non-significant trends (p ≥ 0.05).
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Figure 4. Decadal temperature (a) and dew point temperature (b) anomalies (°C) for DM, MM, DT and MT days across the study stations, evaluated at 09:00 UTC (3:00 PM BST) and computed relative to the 1960–1999 climatological baseline, represented by dashed lines (---).
Figure 4. Decadal temperature (a) and dew point temperature (b) anomalies (°C) for DM, MM, DT and MT days across the study stations, evaluated at 09:00 UTC (3:00 PM BST) and computed relative to the 1960–1999 climatological baseline, represented by dashed lines (---).
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Table 1. Climatological averages (1991–2020) and Koppen classification for study stations.
Table 1. Climatological averages (1991–2020) and Koppen classification for study stations.
StationTemp (°C)Precipitation (mm)Koppen Climate Class
Chittagong26.102486.02Tropical monsoon (Am)
Khulna26.361757.78Tropical without dry season (Af)
Sylhet24.403227.65Humid subtropical (Cwa)
Source: Annual mean temperature and precipitation from World Bank Group (2024) [41]; Koppen climate classes from Mahmud et al. [40].
Table 2. Mean monthly SSC weather-type frequencies (% of days 1960–2024). The average of cross-station monthly means is reported for the dominant types only (DM, MM, MT). Stations are expressed as Chittagong (C), Khulna (K) and Sylhet (S).
Table 2. Mean monthly SSC weather-type frequencies (% of days 1960–2024). The average of cross-station monthly means is reported for the dominant types only (DM, MM, MT). Stations are expressed as Chittagong (C), Khulna (K) and Sylhet (S).
DPDMAverage DMDTMPMMAverage MMMTAverage MTTRStation
January0.763.255.21.30.85.75.722.129.16.1C
0.755.7 1.426.1 27.4 6.7K
0.646.8 5.10.25.4 37.7 4.3S
February0.749.341.85.817.56.326.634.39.2C
0.543.1 6.217.2 30.7 11.2K
0.433.1 11.10.14.1 45.7 5.4S
March0.124.618.112.51.59.67.940.350.511.5C
018.9 11.11.58.7 47.2 12.6K
010.8 10.20.55.5 64 9S
April0.18.45.98.71.517.114.555.665.48.6C
0.13.9 5.41.318 63.3 8.1K
05.4 2.61.38.4 77.2 5.2S
May03.72.53.80.422.720.761.666.67.7C
01.9 3.70.220.5 66.5 7.2K
01.9 2.90.219 71.6 4.5S
June00.60.40.80.634.138.757.453.86.5C
00.2 0.90.135.5 57.3 6.1K
00.4 0.70.346.6 46.7 5.5S
July01.30.90.31.44044.750.446.46.7C
00.3 0.90.541.4 50.1 6.8K
01.1 1.50.752.8 38.6 5.4S
August01.810.1140.743.949.847.96.5C
00.4 0.40.739.2 51.9 7.4K
00.8 0.70.751.9 42.1 3.9S
September03.11.50.50.238.437.951.453.46.5C
00.8 10.334.8 56.2 7K
00.7 0.5040.5 52.6 5.7S
October014.810.51.30.426.825.952574.8C
010.8 2.10.523.8 57.8 5.1K
05.8 1.80.327.3 61.1 3.8S
November040.935.11.30.415.413.538.244.73.7C
035.5 2.50.311.3 45.5 4.9K
028.8 2.80.313.6 50.6 3.9S
December0.661.552.30.518.58.524.132.43.8C
0.352.7 0.71.49.1 31.2 4.7K
0.342.6 2.50.28 41.9 4.5S
Total0.222.718.73.10.922.322.444.248.56.8C
0.118.6 30.821.4 48.8 7.3K
0.114.8 3.50.423.7 52.5 5.1S
Table 3. Interannual trends of SSC weather types (1960–2024). Slope (Δ) shows the rate of change in frequency (% per decade) with corresponding 95% Confidence Interval (CI) of the slopes (only for dominant types). Associated p-values reflect the significance of the slope (bold = OLS p < 0.05; italics (MMK) p < 0.05).
Table 3. Interannual trends of SSC weather types (1960–2024). Slope (Δ) shows the rate of change in frequency (% per decade) with corresponding 95% Confidence Interval (CI) of the slopes (only for dominant types). Associated p-values reflect the significance of the slope (bold = OLS p < 0.05; italics (MMK) p < 0.05).
DPDMDTMPMMMTTR
Chittagongr2 0.827
Δ−0.000−1.822;
CI: [−2.398, −1.133]		+0.208−0.172−3.650;
CI: [−4.192, −3.000]		5.785;
CI: [5.120, 6.451]		−0.548
p0.040<0.0010.104<0.001<0.001<0.0010.003
Khulnar2 0.802
Δ−0.000−1.710;
CI: [−2.315, −1.249]		+0.000−0.095−2.750;
CI: [−3.279, −2.204]		5.290;
CI: [4.628, 5.953]		−0.803
p0.038<0.0010.882<0.001<0.001<0.001<0.001
Sylhetr2 0.733
Δ−0.000−1.343;
CI: [−1.875, −0.843]		+0.416−0.064−4.323;
CI: [−5.162, −3.500]		5.080;
CI: [4.310, 5.851]		−0.062
p0.036<0.0010.042<0.001<0.001<0.0010.679
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Sumaya, N.T.; Senkbeil, J.C.; Sheridan, S.C. Assessing Climate Trends in Bangladesh Using the Spatial Synoptic Classification. Climate 2025, 13, 222. https://doi.org/10.3390/cli13110222

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Sumaya NT, Senkbeil JC, Sheridan SC. Assessing Climate Trends in Bangladesh Using the Spatial Synoptic Classification. Climate. 2025; 13(11):222. https://doi.org/10.3390/cli13110222

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Sumaya, N. T., Senkbeil, J. C., & Sheridan, S. C. (2025). Assessing Climate Trends in Bangladesh Using the Spatial Synoptic Classification. Climate, 13(11), 222. https://doi.org/10.3390/cli13110222

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