Highlights What are the main findings? Tropical precipitation systems (TPSs) exhibit distinct land–ocean differences: continental TPSs have higher vertical development and stronger precipitation intensity, while oceanic TPSs show higher convective area fraction. The stronger precipitation over land is primarily driven by higher droplet collision–coalescence efficiency in the liquid layer, with melting of large ice particles induced by strong updrafts further enhancing the collision–coalescence efficiency. What are the implications of the main findings? These findings clarify the key physical mechanisms underlying land–ocean differences in tropical heavy precipitation, advancing the understanding of TPS formation and evolution. The study provides critical observational constraints for optimizing cloud microphysical parameterization schemes in numerical models, particularly for simulating collision–coalescence and ice-phase processes. Abstract The aim of this work was to reveal the differences in the macro- and microphysical characteristics and precipitation mechanisms of tropical precipitation systems (TPSs) in different regions. Based on the GPM satellite observation from 2014 to 2022, global TPSs were identified, and eight high-frequency areas were defined. Subsequently, their horizontal and vertical development, precipitation characteristics, and microphysical vertical structure were systematically analyzed. The results show that the horizontal development scale of TPSs is mostly between 10 4 and 10 5 km 2, with vertical development exceeding 10 km. The convective area fraction (CAF) ranges from 20% to 60%, and TPSs have a higher CAF and lower vertical development over the ocean than over land. Continental TPSs exhibit significantly stronger vertical development and more intense precipitation in convective cores than oceanic TPSs. The stronger vertical development over land is mainly attributed to stronger updrafts associated with topographic lifting, which further enhances ice-phase microphysical processes and increases ice particle size. Meanwhile, the intensified updrafts also lead to higher collision–coalescence efficiency in the liquid layer, and temperature perturbations over land further enhance turbulent collision efficiency. Together, these processes result in stronger precipitation intensity in the convective cores of continental TPSs. Stratiform regions are characterized by weak precipitation dominated by raindrop breakup with small regional differences. These findings clarify the key land–ocean disparities in TPSs and provide critical observational evidence for optimizing cloud microphysical parameterization schemes in numerical models. Author Contributions Conceptualization, Y.C. and D.W.; methodology, Y.C. and D.W.; validation, X.Z., E.L., L.Y. and Y.G.; formal analysis, Y.C.; investigation, Y.C.; resources, D.W.; data curation, Y.C.; writing—original draft preparation, Y.C.; writing—review and editing, X.Z., E.L., Y.X. and R.X.; visualization, Y.C.; supervision, D.W.; project administration, D.W. and Y.G.; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript. Funding This research was jointly funded by the Science and Technology Development Fund of Macao Special Administrative Region (Grant No. 0009/2024/RIB1) and Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004). Data Availability Statement The GPM DPR Precipitation Profile L2A 1.5 Hours 5 km V07 datasets that support the findings of this study are openly available in the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) at http://doi.org/10.5067/GPM/DPR/GPM/2A/07. Acknowledgments This work was jointly supported by the Science and Technology Development Fund of Macao Special Administrative Region (Grant No. 0009/2024/RIB1) and Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004). We are grateful to the NASA Goddard Space Flight Center’s Mesoscale Atmospheric Processes Laboratory and Precipitation Processing System (PPS) for providing the GPM DPR data. The GPM DPR data are available at https://gpm.nasa.gov/data/directory (accessed on 25 March 2026). Conflicts of Interest Authors Donghai Wang and Yangjinxi Ge were employed by the company COMAC Shanghai Aircraft Flight Test Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abbreviations The following abbreviations are used in this manuscript: TPS Tropical Precipitation System CAF Convective Area Fraction GPM Global Precipitation Measurement DPR Dual-Frequency Precipitation Radar DSD Droplet Size Distribution PR Precipitation Rate LWP Liquid Water Path IWP Ice Water Path MCP Major Convective Pillar PWC Precipitation Water Content PCR Precipitation Conversion Rate ITCZ Inter-Tropical Convergence Zone Z eRadar Reflectivity Factor D mVolume-Weighted Mean Diameter N wA Parameter Proportional to the Number Density of the Particle Size Distribution CCL Connected-Component Labeling Appendix A Figure A1. Normalized frequency pattern of ( a, b) mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 90th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Figure A1. Normalized frequency pattern of ( a, b) mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 90th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Figure A2. Normalized frequency pattern of ( a, b) mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 95th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Figure A2. Normalized frequency pattern of ( a, b) mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 95th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Table A1. Slopes of the 90th percentile SSBs fitting lines of TPSs in different regions. Table A1. Slopes of the 90th percentile SSBs fitting lines of TPSs in different regions. ୍ଠ୧୦ −3 m 3 kg −1 h −1LWP IWP Conv. Stra. Conv. Stra. All 4.87 4.29 10.03 −0.89 IO 5.10 4.56 11.45 \ WP 4.78 4.42 9.36 \ CP 4.87 4.48 12.63 −1.23 EP 4.86 4.51 14.45 \ AT 5.13 4.50 14.06 \ SA 5.17 4.40 10.10 \ CA 5.77 4.31 8.04 \ MC 4.93 4.35 8.46 \ Table A2. Similar to Table A1, but for the 95th percentile SSBs. Table A2. Similar to Table A1, but for the 95th percentile SSBs. ୍ଠ୧୦ −3 m 3 kg −1 h −1LWP IWP Conv. Stra. Conv. Stra. All 4.74 4.30 12.22 −1.58 IO 4.88 4.55 14.19 \ WP 4.68 4.49 9.49 \ CP 4.56 4.46 13.84 \ EP 4.77 4.53 13.20 \ AT 4.85 4.57 14.82 \ SA 5.01 4.44 11.37 \ CA 5.62 4.54 8.25 \ MC 4.76 4.35 10.92 \ References Medeiros, B.; Clement, A.C.; Benedict, J.J.; Zhang, B. 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An example of a TPS on 29 June 2022: ( a) PR, ( b) precipitation type classification (red shading: convective precipitation areas, blue shading: stratiform precipitation areas, white shading: other types), ( c) along-track radar reflectivity profile of the precipitation center, ( d) cross-track radar reflectivity profile of the precipitation center, ( e) frequency distribution of TPSs from 2014 to 2022. Figure 1. An example of a TPS on 29 June 2022: ( a) PR, ( b) precipitation type classification (red shading: convective precipitation areas, blue shading: stratiform precipitation areas, white shading: other types), ( c) along-track radar reflectivity profile of the precipitation center, ( d) cross-track radar reflectivity profile of the precipitation center, ( e) frequency distribution of TPSs from 2014 to 2022. Figure 2. Boxplots of ( a) horizontal development areas, ( b) development areas of MCPs, ( c) CAF, ( d) storm top height, ( e) mean −40 °C height, ( f) mean 0 °C height, ( g) mean LWP, and ( h) mean IWP of TPSs in the entire tropics and 8 regions. Box colors: gray for all TPSs, blue for ocean, red for land, and green for the Maritime Continent. Figure 2. Boxplots of ( a) horizontal development areas, ( b) development areas of MCPs, ( c) CAF, ( d) storm top height, ( e) mean −40 °C height, ( f) mean 0 °C height, ( g) mean LWP, and ( h) mean IWP of TPSs in the entire tropics and 8 regions. Box colors: gray for all TPSs, blue for ocean, red for land, and green for the Maritime Continent. Figure 3. The frequency differences in PR in ( a) all areas, ( b) convective areas, ( c) stratiform areas, and ( d) MCPs of TPSs between 8 regions and the entire tropics. The frequency difference is calculated as the difference between the frequency distribution of profiles under given precipitation conditions in each region and the mean frequency over the global tropics. Figure 3. The frequency differences in PR in ( a) all areas, ( b) convective areas, ( c) stratiform areas, and ( d) MCPs of TPSs between 8 regions and the entire tropics. The frequency difference is calculated as the difference between the frequency distribution of profiles under given precipitation conditions in each region and the mean frequency over the global tropics. Figure 4. Boxplots of ( a) PR max and ( b) mean PCR of TPSs in the entire tropics and 8 regions. Box colors: gray is used for all TPSs, blue for ocean, red for land, and green for the Maritime Continent. Figure 4. Boxplots of ( a) PR max and ( b) mean PCR of TPSs in the entire tropics and 8 regions. Box colors: gray is used for all TPSs, blue for ocean, red for land, and green for the Maritime Continent. Figure 5. Normalized frequency pattern of ( a, b) of mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 99th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Figure 5. Normalized frequency pattern of ( a, b) of mean LWP-PR and ( c, d) IWP-PR in ( a, c) convective and ( b, d) stratiform areas of TPSs. The symbol “×” in ( a– d) represents the 99th percentile significant sample bins (SSBs). The gray lines are the linear fitting lines of the SSBs, and k is the slope of the fitting lines (only the fitting lines and their slopes significant at the α = 0.05 level are presented). Fitting lines of ( e, f) LWP-PR and ( g, h) IWP-PR SSBs in ( e, g) convective and ( f, h) stratiform areas of TPSs. Figure 6. The normalized frequency pattern of Z e in ( a) all areas, ( b) stratiform areas, ( c) convective areas, and ( d) MCPs of TPSs. Figure 6. The normalized frequency pattern of Z e in ( a) all areas, ( b) stratiform areas, ( c) convective areas, and ( d) MCPs of TPSs. Figure 7. The normalized frequency pattern of ( a, b) D m and ( c, d) N w in ( a, c) stratiform areas and ( b, d) MCPs of TPSs. Figure 7. The normalized frequency pattern of ( a, b) D m and ( c, d) N w in ( a, c) stratiform areas and ( b, d) MCPs of TPSs. Figure 8. Mean profiles of ( a, b) D m and ( c, d) N w in ( a, c) stratiform areas and ( b, d) MCPs of TPSs over different regions. Figure 8. Mean profiles of ( a, b) D m and ( c, d) N w in ( a, c) stratiform areas and ( b, d) MCPs of TPSs over different regions. Figure 9. Normalized frequency pattern of PWC-D m in MCPs at ( a) 0.5 km, ( b) 2 km, and ( c) 4 km. ( d) The 99th percentile SSBs at the 3 levels. Fitting lines of SSBs for ( e) the low-D m mode and ( f) high-D m mode at 2 km, and ( g) at 4 km in MCPs across different regions. Gray curves labeled by N 1–N 6 represent reference lines under number concentration conditions of 1 × 10 4, ୨ ୍ଠ ୧୦ 4, ୫ ୍ଠ ୧୦ 4, ୧ ୍ଠ ୧୦ 5, ୨ ୍ଠ ୧୦ 5 and 5 × 10 5 m −3. Figure 9. Normalized frequency pattern of PWC-D m in MCPs at ( a) 0.5 km, ( b) 2 km, and ( c) 4 km. ( d) The 99th percentile SSBs at the 3 levels. Fitting lines of SSBs for ( e) the low-D m mode and ( f) high-D m mode at 2 km, and ( g) at 4 km in MCPs across different regions. Gray curves labeled by N 1–N 6 represent reference lines under number concentration conditions of 1 × 10 4, ୨ ୍ଠ ୧୦ 4, ୫ ୍ଠ ୧୦ 4, ୧ ୍ଠ ୧୦ 5, ୨ ୍ଠ ୧୦ 5 and 5 × 10 5 m −3. Figure 10. Frequency patterns of ΔZ e-ΔD m in MCPs of TPSs over ( a) the Indian Ocean, ( b) the Western Pacific, ( c) the Central Pacific, ( d) the Eastern Pacific, ( e) the Atlantic, ( f) South America, ( g) Central Africa, and ( h) the Maritime Continent. Figure 10. Frequency patterns of ΔZ e-ΔD m in MCPs of TPSs over ( a) the Indian Ocean, ( b) the Western Pacific, ( c) the Central Pacific, ( d) the Eastern Pacific, ( e) the Atlantic, ( f) South America, ( g) Central Africa, and ( h) the Maritime Continent. Figure 11. Normalized frequency pattern of PWC-D m in stratiform areas at ( a) 0.5 km, ( b) 2 km, and ( c) 4 km. Fitting lines of SSBs for PWC-D m frequency pattern at ( d) 0.5 km, ( e) 2 km, and ( f) 4 km in stratiform areas across different regions. Gray curves labeled by N 1–N 6 represent reference lines under number concentration conditions of 1 × 10 4, ୨ ୍ଠ ୧୦ 4, ୫ ୍ଠ ୧୦ 4, ୧ ୍ଠ ୧୦ 5, ୨ ୍ଠ ୧୦ 5 and 5 × 10 5 m −3. Figure 11. Normalized frequency pattern of PWC-D m in stratiform areas at ( a) 0.5 km, ( b) 2 km, and ( c) 4 km. Fitting lines of SSBs for PWC-D m frequency pattern at ( d) 0.5 km, ( e) 2 km, and ( f) 4 km in stratiform areas across different regions. Gray curves labeled by N 1–N 6 represent reference lines under number concentration conditions of 1 × 10 4, ୨ ୍ଠ ୧୦ 4, ୫ ୍ଠ ୧୦ 4, ୧ ୍ଠ ୧୦ 5, ୨ ୍ଠ ୧୦ 5 and 5 × 10 5 m −3. Figure 12. Frequency patterns of ΔZ e-ΔD m in stratiform areas of TPSs over ( a) the Indian Ocean, ( b) the Western Pacific, ( c) the Central Pacific, ( d) the Eastern Pacific, ( e) the Atlantic, ( f) South America, ( g) Central Africa, and ( h) the Maritime Continent. Figure 12. Frequency patterns of ΔZ e-ΔD m in stratiform areas of TPSs over ( a) the Indian Ocean, ( b) the Western Pacific, ( c) the Central Pacific, ( d) the Eastern Pacific, ( e) the Atlantic, ( f) South America, ( g) Central Africa, and ( h) the Maritime Continent. Figure 13. ( a) Normalized frequency distribution of mean D m below 2 km as a function of ∆T in the MCPs. Black “×” markers denote the D m frequency peak under each ∆T condition. ( b) Curves connecting the ∆T-D m frequency peak points for different regions. Figure 13. ( a) Normalized frequency distribution of mean D m below 2 km as a function of ∆T in the MCPs. Black “×” markers denote the D m frequency peak under each ∆T condition. ( b) Curves connecting the ∆T-D m frequency peak points for different regions. Figure 14. ( a) Proportion of TPSs fully contained within the FS swath, and ( b) variation curves of TPSs occurrence frequency with horizontal pixel number. Shading indicates the maximum difference across different regions. Figure 14. ( a) Proportion of TPSs fully contained within the FS swath, and ( b) variation curves of TPSs occurrence frequency with horizontal pixel number. Shading indicates the maximum difference across different regions. Table 1. Division of tropical regions and the number of TPSs and GPM profiles. Table 1. Division of tropical regions and the number of TPSs and GPM profiles. Type Region Location Number of TPSs GPM Profiles Ocean Indian Ocean (IO) 15°S–10°N, 45–95°E 36,657 16,342,146 Western Pacific (WP) 15°S–15°N, 150–180°E 36,330 16,805,406 Central Pacific (CP) 20°S–15°N, 130–180°W 44,514 19,218,723 Eastern Pacific (EP) 0–15°N, 80–130°W 27,336 11,316,995 Atlantic (AT) 5°S–10°N, 5°E–45°W 22,864 8,651,519 Land South America (SA) 15°S–10°N, 45–80°W 34,878 13,745,112 Central Africa (CA) 15°S–15°N, 5–45°E 25,432 10,236,590 Maritime Continent (MC) 15°S–25°N, 95–150°E 79,492 35,564,202 Table 2. Frequency of profiles within different PR ranges for TPSs (unit: %). “All” denotes the entire TPSs, “Conv.” denotes the convective area, “Stra.” denotes the stratiform area, and MCP denotes the MCP of the TPS. Table 2. Frequency of profiles within different PR ranges for TPSs (unit: %). “All” denotes the entire TPSs, “Conv.” denotes the convective area, “Stra.” denotes the stratiform area, and MCP denotes the MCP of the TPS. PR (mm h −1) 0–1 1–2.5 2.5–5 5–10 10–20 20–50 50–100 100+ All 48.46 24.77 13.50 7.47 3.21 1.91 0.52 0.16 Conv. 34.35 24.14 15.21 11.06 7.02 5.71 1.91 0.59 Stra. 51.12 25.65 13.38 6.64 2.19 0.85 0.13 0.03 MCP 22.53 22.14 16.79 13.66 9.99 9.65 3.71 1.54 Table 3. Slopes of the fitting lines of TPSs in different regions. Table 3. Slopes of the fitting lines of TPSs in different regions. ୍ଠ୧୦ −3 m 3 kg −1 h −1LWP IWP Conv. Stra. Conv. Stra. All 4.51 4.47 19.09 \ IO 4.59 4.37 16.56 \ WP 4.62 4.19 12.44 −3.15 CP 4.18 4.41 25.05 \ EP 4.29 4.62 29.98 \ AT 4.62 4.52 20.07 \ SA 4.84 4.52 13.00 \ CA 4.57 3.67 8.29 \ MC 4.57 4.50 11.43 \ Table 4. The proportion of TPSs fully contained within the FS swath in different regions. Table 4. The proportion of TPSs fully contained within the FS swath in different regions. % All IO WP CP EP AT SA CA MC Ratio 37.34 36.72 36.19 38.50 36.74 36.63 41.58 42.56 37.05 Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
