Seasonal regression with temperature data

Question

I have a daily collected time-series of temperature data that goes from 1981 to 2018 that look like this:

head(extracted_data)
   value          t quarter year
1 298.92 1981-08-25      Q3 1981
2 298.20 1981-08-26      Q3 1981
3 297.78 1981-08-27      Q3 1981
4 298.14 1981-08-28      Q3 1981
5 297.50 1981-08-29      Q3 1981
6 298.25 1981-08-30      Q3 1981

I'd like to build a regression model to get the annual change of temperature. In order to consider the seasonality of temperature, I built the model as the following:

model <- lm(value ~ quarter + year, data=extracted_data)

which gives me this:

summary(model)

Call:
lm(formula = value ~ quarter + year, data = extracted_data)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.5027  -1.2918  -0.0495   1.1469   9.4846 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 2.060e+02  3.596e+00   57.28   <2e-16 ***
quarterQ2   4.506e+00  5.532e-02   81.45   <2e-16 ***
quarterQ3   1.161e+01  5.501e-02  210.97   <2e-16 ***
quarterQ4   4.909e+00  5.481e-02   89.56   <2e-16 ***
year        4.070e-02  1.798e-03   22.63   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.265 on 13638 degrees of freedom
Multiple R-squared:  0.7711,    Adjusted R-squared:  0.771 
F-statistic: 1.148e+04 on 4 and 13638 DF,  p-value: < 2.2e-16

From this, can I say that the temperature is expected to increase by 0.0407 Kelvin degrees every year? Is the approach correct? I'm sorry I'm not a statistician!