| 20 May 2026
Atmospheric Rivers landfalling in Japan: Climatology and physical characteristics causing heavy rainfall
Abstract. This study investigates the climatology and physical factors governing the precipitation efficiency of Atmospheric Rivers (ARs) landfalling in Japan. Using an ERA5 reanalysis-based AR database (1940–2023), we identified typical synoptic patterns of landfalling ARs via Self-Organizing Maps, which effectively categorize the regional moisture transport pathways. Climatological analysis revealed a significant increasing trend in AR frequency specifically in northern Japan. We further examined the relationship between AR characteristics and rainfall using nationwide high-resolution observations. While Integrated Water Vapor Transport (IVT) explains a substantial portion of the overall relationship (R = 0.71), considerable variability in precipitation amounts remains for similar IVT levels. Our analysis demonstrates that rainfall intensity is primarily modulated by a combination of strong moisture convergence (MVIMC), low convective inhibition (CIN), and orographic enhancement over high elevations. Furthermore, precipitable water (PW) emerged as the critical differentiator for the formation of quasi-stationary linear rainbands (QSLRBs), which consistently develop in close proximity to the AR axis. These findings suggest that the synoptic-scale AR provides the necessary environmental conditions for the organization of mesoscale extremes. Enhancing the predictive accuracy of AR landfall location and internal structure is thus a crucial prerequisite for improving the predictability of catastrophic localized rainfall in East Asia.
The paper shows that IVT alone is not enough to explain heavy rainfall from ARs in Japan, using SOM based AR classification, Huber regression residual grouping, and a QSLRB overlap analysis. The Mann Kendall trend result (AR frequency rising in northern Japan) and the finding that PW separates QSLRB from non QSLRB events are the strongest parts of the paper. But the statistics connecting Figure 4 and 5 to the paper's main claim are not strong enough to support how confidently the claim is stated. The paper also never looks at vertical circulation structure even though its own explanation depends on convergence and lifting. And the AR to rainfall relationship is only checked as same window overlap, with no check for whether rainfall happens before, during, or after AR landfall. Major revision is recommended.
Major Comments:
1. In lines 219-238, Figure 4. Ten separate tests are run (IVT, PW, CAPE, CIN, MVIMC, RH500, latitude, duration, overlap, elevation) on groups of 46 and 50 events, with no correction for running that many tests at once. At a 0.05 significance level across 10 tests, you would expect about one false positive purely by chance. Five out of ten come back significant here. Without a correction such as Bonferroni or FDR, this result cannot be trusted as strongly as the paper treats it, and it is central to the main claim of the abstract.
2. In lines 198-217. The way events are split into positive and negative residual groups may already guarantee part of the result. IVT and PW are physically related to each other. When you regress rainfall on IVT and look at what is left over, you also remove any effect that comes from things correlated with IVT, including PW. So, the finding that "PW shows no difference between groups" (line 222) might just be a side effect of how the groups were built, not real evidence that PW does not matter. The authors should report the correlation between IVT and PW directly, and discuss whether their method could even detect a PW effect if one existed.
3. In lines 219-238. MVIMC, CIN, RH500, elevation, and latitude are treated as five separate independent factors, but some of these are likely connected. Higher elevation naturally increases convergence through orographic lifting, so elevation and MVIMC may be reflecting the same physical process rather than two separate ones. A partial correlation or multiple regression is needed before listing both as separate conclusions (lines 326 to 328).
4. In lines 141-143. The choice of a 2 by 2 SOM grid is only explained in words, not backed by numbers. Since Cluster D at 44.7 % of events (line 143) is the basis for the whole climatology section, the grid size choice needs a quantitative check, such as quantization error or topographic error across different candidate grid sizes.
5. In Section 3.1 and 3.2, Figures 1, 5, 6. All circulation analysis in the paper is based only on IVT composites. The discussion (lines 288 to 297) argues that convergence and low CIN drive rainfall, but this is inferred from a single CIN number rather than shown directly. Adding vertical velocity and geopotential height anomaly composites at 500 hPa and 850 hPa for the positive and negative residual groups, alongside the existing IVT panels, would let the authors actually show this mechanism instead of inferring it. This kind of analysis is used in Pandidurai et al. (2026), https://doi.org/10.1002/joc.70462.
6. In lines 96-128 and 245-248. The relationship between ARs and rainfall is only checked through overlap in time and space (lines 96-99, 127-128), never through timing offset. Since both ARs and QSLRBs last several hours on average (Figure 2b shows 10 to 13.5 hours), simple overlap cannot tell apart rainfall that happens at the same time as the AR from rainfall that happens as a delayed response, for example moisture building up first and heavy convection organizing later. The authors should compute a lag relationship, either a cross correlation or a distribution of the time gap between peak rainfall or QSLRB onset and AR axis passage, going out to at least 48 hours given the six hourly resolution of the data. This should be done separately for each SOM cluster and for QSLRB versus non QSLRB events before combining everything, because pooling all 479 events together first could hide a real lag if different AR types behave differently. This connects to the sequential moisture approach in Pandidurai et al. (2026), cited above in M5.
Minor Comments
1. Lines 218 to 221: The sentence describing what Figure 4 shows is repeated almost word for word right after itself. Remove the duplicate.
2. Lines 65 to 68: "we utilized a suite of ERA5 based variables and indices except for precipitation" is awkward. Consider "excluding precipitation, which came from RA data instead."
3. Lines 219, 260 (Figure 4, Figure 5c): The statistical test used to get the p values is never named. Since the paper already points out that rainfall data has heavy tails and uses Huber regression for that reason (lines 108 to 111), using a plain t test elsewhere would be inconsistent. State and justify whichever test was used.
4. Line 232: "Landfalling Elevation" is never clearly defined. Is it the elevation at the point where the AR axis meets the coast, or the average terrain elevation across the AR footprint? Needs one clarifying sentence.
5. Lines 51, 172, 320 and reference list: The name "Hirockawa" is used throughout, but the more common spelling in the AR and QSLRB literature is "Hirokawa." Worth checking if this is correct or a repeated typo.
6. Lines 245 to 250: State clearly that the QSLRB analysis only covers 2006 to 2023 because of the RA data limits, while the residual group analysis in section 3.2.1 covers a different period. This is currently left unstated.
7. The authors note that the causes behind shifting AR tracks remain unclear (lines 315–317). One direction for future work would be tracing moisture sources for landfalling AR events and using higher time resolution moisture budget datasets to better capture the fine-scale convergence signal discussed as central to rainfall efficiency in Section 3.2.1. Relevant studies include: https://doi.org/10.1029/2026EF008305, https://doi.org/10.1029/2026GL121973, and https://doi.org/10.1038/s41597-025-06044-y.