Construction of homogenized daily surface air 1 temperature for Tianjin city during 1887-2019 2

. The century-long continuous daily observations from some stations are important for the 31 study of long-term trends and extreme climate events in the past. In this paper, three daily data sources: 32 (1) Department of Industry Agency of British Concession in Tianjin covering Sep 1 1890-Dec 31 1931 33 (2) Water Conservancy Commission of North China covering Jan 1 1932-Dec 31 1950 and (3) monthly 34 journal sheets for Tianjin surface meteorological observation records covering Jan 1 1951-Dec 31 2019 35 have been collected from the Tianjin Meteorological Archive. The completed daily maximum and 36 minimum temperature series for Tianjin from Jan 1 1887 (Sep 1 1890 for minimum) to Dec 31 2019 has 37 been constructed and assessed for quality control and an early extension from 1890 to 1887. Several 38 significant breakpoints are detected by the Penalized Maximal T-test (PMT) for the daily maximum and 39 minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth, 40 CRUTS4.03 and GHCNV3 data. Using neighboring daily series the record has been homogenized with 41 Quantile Matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual 42 mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum 43 temperature is evaluated as 0.154±0.013°C decade -1 during the last 130 years. Trends of temperature 44 extremes in Tianjin are all significant at the 5% level, and have much more coincident change than those 45 from the raw, with amplitudes of -1.454 d decade -1 , 1.196 d decade -1 , -0.140 d decade -1 and 0.975 d 46 decade -1 for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at 47 the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for 48 Tianjin city presented here is publicly available at https://doi.pangaea.de/10.1594/PANGAEA.924561 49 (Si and Li, 2020). on to remove any errors caused by manual observations, instrument malfunctions and digital inputs.

Since daily time series generally contain many more observations than monthly or annual series, 85 daily analyses potentially have greater precision. As a result they are more useful in climate trend and 86 variability studies, especially for extreme events (Vincent et al., 2012;Xu et al., 2013;Trewin, 2013;87 Hewaarachchi et al., 2017). However, due to difficulties in collecting and/or receiving daily data all over 88 the world as well as non-climatic effects such as changes in observation times, there are numerous issues. 89 For example, observations from temperature sites at principal stations in Canada were changed to be 90 read at 0000UTC to 0600UTC (Vincent et al., 2002), making it is very difficult to form a global daily 91 data product at century-long scales. This makes it extremely difficult to study global and/or regional 92 extreme events over the past hundred years, especially before 1950. For some regional areas, daily  series of maximum and minimum temperatures for Tianjin city (see Fig. 2). As shown in Table 1    between Tianjin station and Xiqing station that happened at Jan 1 1992 (Table 1)   Meteorological Archive began with September 1890 (Fig. 2). Therefore, some additional reference data 206 sources are selected to extend the daily temperature series from January 1887 to August 1890 and 207 lengthen the established daily temperature data to as early as possible. In addition, it is extremely 208 important to establish an objective as well as a reasonable reference series for data homogenization. But

222
The selected three LSAT are not independent as they likely use common input observations. The         Secondly, we will select 10 stations those are closest to Tianjin station using a spherical distance; Finally, 276 9 stations are confirmed which are consistent between step 1 and step 2. In Figure 5

288
The RHtestsV4 software package is used to homogenize the daily maximum and minimum temperature , homogenization at the daily timescales is much more challenging than that at 293 monthly or annual scales. Thus, firstly we test Tianjin's monthly observed maximum and minimum 294 temperature series averaged from the daily ones to find the significant breakpoints by means of PMT at 295 the 5% significance level using two types of monthly reference series. We then adjust the daily series at 296 Tianjin station by QM-adjustment with or without the daily reference series.

297
The breakpoints in the segment before 1921 are mainly determined by objective judgment from the 298 same shifts at the two monthly timescales simultaneously due to the scarcity of station metadata. Those after 1921 are additionally assessed together with the station metadata and PMT detection at the 5% 300 significance level. According to Table 1, we made a list containing some possibilities that could cause 301 shifts in Tianjin's daily maximum and minimum temperature series (Fig. 2 vertical dashed lines). The       Table 4 provides the definition of temperature extremes (Zhang et al., 2011). They are calculated 365 based on the newly constructed and raw series (after quality control and extension) in Tianjin. As shown 366 in Fig. 9, the number of TN10p (Fig. 9a) and TX10p (Fig. 9b) at the monthly timescales are increased by from newly data are all less than those from the raw data, with the number of days decreased by 3.1-6.1.

370
This is mainly due to large positive adjustments applied to daily maximum temperature in these months 371 (Table 3). In the opposite sense, the number of TN90p (Fig. 9c) and TX90p (Fig. 9d)   to August the number is decreased by 11.8-14.8 days. This is due to the large negative adjustments 375 applied to daily maximum temperature in these months (Table 3). The number of TN10p (Fig. 9a) from 376 newly constructed series at the annual timescales are increased by 1.1 days compared to the raw ones 377 while for TX10p (Fig. 9b), TN90p (Fig. 9c) and TX90p (Fig. 9d) Table 6 indicates that trends of temperature extremes based on the newly constructed series are all   TX10p, TN90p and TX90p are all significant at the 5% level, and they give a much more consistent set 446 of trends. To some extent, changes in climate extremes can be analyzed with higher confidence using the 447 newly constructed daily data in this paper. 448 However, in the current study, there may be some systematic biases (possibly some potential