| 25 Feb 2026
Development of a global in-situ daily temperature dataset preferentially for 0000–2400 UTC from 1981–2024
Abstract. Large amounts of sub-daily temperature data are shared globally through the Global Telecommunication System (GTS) in near-real time and through international data exchanges. However, converting these data into a global daily temperature dataset with a uniform definition – especially for daily maximum (Tmax) and minimum (Tmin) temperatures – has proven challenging due to the independent observation schedules across the world. To address this issue, we developed a new method that decomposes sub-daily Tmax and Tmin records from the Integrated Surface Database (ISD) into finer intervals, subsequently reaggregating them into daily Tmax and Tmin based on prospective dateline. This new method increased the global daily Tmax and Tmin counts by 64 % and 45 %, respectively, compared to the original method, which relied on either two consecutive Tmax/Tmin records over 12 hours or a single Tmax/Tmin record over 24 hours. The Global Land Base Dataset-First Estimate Daily Data (GLBD-FED) was established for the period from 1981 to 2024, following corrections for misrecorded Tmax and Tmin and quality control. GLBD-FED includes daily maximum (Tmax), average (Tave), and minimum temperature (Tmin) from approximately 17,000 global sites, with daily data amounts reaching about 10,000 entries per day in the current decade. When compared to the Global Summary of the Day (GSOD) dataset, GLBD-FED exhibits more temperate performance over the last 40 years, showing a slightly lower daily Tmax (around -0.3 °C) and a higher daily Tmin (around +0.3 °C), with a nearly identical daily Tave (approximately +0.1 °C). GLBD-FED identifies records that could or almost could represent the highest or lowest temperature in the 24-hour period as daily Tmax or Tmin, while GSOD selects the highest or lowest records within the 24 hours. This variance in definitions, combined with different preferences for meteorological and airport report sources, contributes to these observed biases.
This is an interesting paper describing a data set of potential significant value. I think it has the makings of a publishable paper but needs some work before getting to that stage.
Although the authors were probably not aware of this when the dataset was being developed, it is unfortunate that both the ISD and GSOD datasets were retired in 2025 (ISD has been replaced by GHCN-Hourly, the replacement for GSOD is not yet online). As such this dataset will not be able to be updated in its current form – this is worth acknowledging, I think.
Major comments
Minor comments
L50-54 – this is a very long list of datasets, but none are particularly recent – suggest being more selective and focus on most recent versions.
L114 – is there any indication of the geographic distribution of different observation times?
L129-130 – needs a cross-reference to the appendix for specifics of the quality control methods used.
Section 4.1 – this looks like a fairly labour-intensive process, how practical is it to implement this across the network?
L184 – 'as discussed in section 3.1' – this matter is not really discussed there. There are some references which could be cited on methods for Tave calculation – see, for example, https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wcc.46 and references therein.
L227 – it is worth noting that some individual countries have a particularly large increase (e.g. Finland is obvious on the max for both Tmax and Tmin).
L245-250 – realistically with the size of the dataset this process would need to be automated – can it be confirmed in the text whether this has been done?
L267-270 – would this be connected with different national practices about whether or not new identifiers are assigned when a site moves?
Figure 7 – it looks like in the timeseries there are some individual months with a sharp drop in the number of available stations, is this correct, and if so is there any explanation for it?
L294-297 – how is the averaging done here – is it an arithmetic mean of stations or area-weighted in some way?
L297-299 – in principle GSOD could be used for extreme events research but in practice I am not aware of any serious climate research which does so (probably because people are well aware of the limitations of the GSOD data).
L397 – 'generally reports warmer temperatures' – this isn't quite correct, what is true is that it generally reports warmer temperatures when there is a difference, but many days (presumably those where GSOD is not double-counting) have zero difference.
Figure 8 – is there any explanation for the seasonal cycle in the early years of these time series?
Figure 9c – it may or may not be relevant to the Australian results here that the standard times for reporting Tmin nationally in eastern Australia is 2200 or 2300 UTC (depending on season), although international synoptic reporting of this is sometimes at 0000 UTC.
Table 2 – the elevations are incorrect here – the general Ulan Bator metropolitan area is at approximately 1300m elevation.