the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Aug 2026
Warm-Sector Heavy Rainfall over the Eastern China Plains: Identification, Characteristics, and Precursor Dynamic Signals
Abstract. Warm-sector heavy rainfall (WSHR) features small spatial scales, posing critical forecast challenges, while its fractional contribution to regional heavy rainfall and its short-term precursors remain poorly quantified. Drawing on 2021–2025 warm-season (June–September) observations from 493 surface rain gauges, ERA5 reanalysis, and a radar wind profiler (RWP) mesonet across the eastern China plains, we build an objective identification framework integrating automated front detection, warm-sector mask, and station-specific 99th-percentile hourly rainfall thresholds to separate WSHR from other heavy rain events. We detect 477 station-based WSHR events alongside 6002 non-warm-sector heavy rainfall events. WSHR exhibits scattered spatial distribution, peaks in July–August, and preferentially occurs overnight, with rainfall onset concentrated 2000–0300 Beijing Time. Accounting for merely 5–10 % of annual heavy rainfall occurrences, WSHR has shorter durations yet comparable median rainfall accumulations and heavier extreme rainfall tails. On average, they contributed 13.8 % of the total heavy-rainfall accumulation, and locally 40–50 %. Composite ERA5 fields show that WSHR initiates within an 850-hPa moist tongue of high pseudo-equivalent potential temperature south of the front, superimposed on strengthening low-level moisture flux convergence. RWP composites based on 62 WSHR events reveal a characteristically low and strong low-level jet that becomes increasingly frequent before the onset of rainfall, accompanied by enhanced kinetic energy and shallow vertical shear below 3 km and an abrupt ascent increase centered at 1–2 km within the final hour. These kinematic signals from wind profilers offer valuable observational precursors for predicting localized WSHR across the eastern China plains.
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RC1: 'Comment on egusphere-2026-4109', Anonymous Referee #2, 29 Aug 2026
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-4109/egusphere-2026-4109-RC1-supplement.pdfReplyCitation: https://doi.org/
10.5194/egusphere-2026-4109-RC1 -
RC2: 'Comment on egusphere-2026-4109', Anonymous Referee #1, 31 Aug 2026
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General Comments
This study draws on five years (2021–2025) of minute-resolution rain-gauge records, ERA5 reanalysis, and a radar wind profiler (RWP) mesonet to establish an objective identification framework that separates warm-sector heavy rainfall (WSHR) from other heavy rainfall over the eastern China plains, and then to characterize its spatiotemporal distribution, event-scale properties, contribution to the regional heavy-rainfall total, and the low-level dynamic precursors recorded by the RWPs prior to convective onset. The analysis is generally careful and the writing is clear. Some of the conclusions—the nocturnal preference of WSHR, the warm, humid, high-θₑ environment, and the role of the low-level jet—echo previous studies from South China, but a notable contribution of this work is to establish, through a consistent multi-year objective framework, that these features also hold over the eastern China plains, where the coastal forcing central to the South China regime is largely absent. The genuine added value lies in the objective identification and contribution quantification across five years and 493 stations, and in the high-frequency, RWP-resolved time evolution of the pre-onset low-level dynamic environment together with its implications for nowcasting, which give the paper some merit.
Specific Comments
1.Absence of a control group leaves the specificity of the precursor signals undetermined. The authors themselves acknowledge this in Sect. 4.1. All RWP precursor composites are drawn only from WSHR events, so it is not possible to tell whether these signals are specific to WSHR or are simply the generic prelude to warm-season convection—which is precisely what determines whether they can serve as forecast indicators. I suggest the authors either add a brief control composite (for example, the "other heavy rainfall" events at the same co-located RWP stations) or correspondingly temper the forecast-oriented statements in Sects. 4.3 and 5, positioning these signals as "candidate, to-be-evaluated" indicators and stating explicitly that their specificity remains to be established.
2.Section 3.4 observationally provides mainly the LLJ occurrence frequency (Fig. 11c) and the core-height/core-speed scatter (Fig. 11b), yet from these it draws a series of mechanistic assertions—for instance, that the jet generates low-level convergence near its terminus, organizes and sustains convection, and places the strongest convergence and ascent at 1–2 km (L475–482, L563–573). The single metric of a rising occurrence frequency is not by itself sufficient to support this convergence-and-triggering chain of reasoning. I suggest the authors add observational evidence linking the "jet" to "convergence/ascent/triggering," or at least distinguish explicitly in the text between what the observations directly support (the time evolution of LLJ frequency) and what is inferred mechanistically from the existing literature.
3.Because events are grouped by fixed station along time (Sect. 2.3), a single weather process affecting several stations is recorded as multiple events. For the composite analysis, the effective number of independent samples may therefore be smaller than the nominal event count. I suggest the authors: (i) state once in the text that the count is of "station-based events" rather than "independent weather systems"; and (ii) provide, in the main text or the Supplement, a simple distribution of the number of events per station, so that readers can gauge the independence of the composites.
4.Inconsistency between L356–357 and the Fig. 6c legend. The text gives the median cumulative rainfall of other heavy rainfall events as "76.2 mm" (L357), whereas the Fig. 6c legend annotates the same quantity (the other-category P₅₀) as 56.2 mm. Please confirm whether these refer to the same statistic.
Technical corrections
1.Standardize unit notation. Physical-unit notation is mixed throughout: specific humidity is mostly written as "g kg⁻¹" in the text but as "g/kg" on some figure axes/legends (Fig. 7), and wind speed appears both as "m s⁻¹" and "m/s" (Fig. 9 axes/caption). Please adopt a single form as required by the journal.
2.L37: "they contributed 13.8%" is in the past tense, inconsistent with the present tense used throughout the abstract.
3.Sect. 2.4 should define "target station" (L256). The term is used from Sect. 2.4 onward but is never defined in the paper. Please add a sentence at its first occurrence to help the reader.Citation: https://doi.org/10.5194/egusphere-2026-4109-RC2
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